Myocardial ischemia-reperfusion injury (MIRI) is a very common adverse reaction after cardiac valve replacement (CVR) under cardiopulmonary bypass, which seriously affects the rehabilitation and prognosis of patients.  The prevention and treatment of MIRI are a hotspot of modern medical research, and this study is aimed at providing reliable reference and guidance for future clinical prevention and treatment of MIRI by analyzing the effects of ulinastatin (UL) on cardiac function and MIRI of patients after CVR.  A total of 104 patients undergoing CVR under cardiopulmonary bypass in our hospital were selected as research participants. Among them, 52 patients treated with UL were assigned to the observation group, and the rest 52 patients given the same amount of normal saline were assigned to the control group. The cardiopulmonary bypass status, postoperative status, cardiac function, inflammatory response, oxidative stress response, and hemodynamics were observed and compared between the two groups. In addition, clinical efficacy and safety and patient prognosis were compared. P > 0.05) but had obvious protective effects on cardiopulmonary bypass status, cardiac function, inflammation, oxidative stress, and hemodynamics (P < 0.05).  Through experimental analysis, we found that UL had no significant effect on the clinical efficacy, safety, and prognosis of patients after surgery ( UL can effectively prevent the occurrence of MIRI after CVR under cardiopulmonary bypass, which is worthy of clinical application. Clinically, myocardial ischemia-reperfusion injury (MIRI) is a very common cardiovascular disease, mainly due to the fact that although the ischemic myocardium restores its normal perfusion capacity after acute coronary occlusion and recanalization, its tissue damage is progressively aggravated . MIRI isCardiac valve replacement (CVR) under cardiopulmonary bypass is the most common type of cardiovascular surgery. It can restore the impaired blood perfusion ability of patients through artificial heart valves, contributing to good hemodynamic characteristics and a very low incidence of thrombosis , 9. At tIn view of the increasing potential risks of MIRI, this study analyzed the effects of UL on cardiac function and MIRI of patients after CVR under cardiopulmonary bypass, so as to provide reliable reference and guidance for future clinical prevention and treatment of MIRI.A total of 104 patients who underwent CVR under cardiopulmonary bypass in Yijishan Hospital, the First Affiliated Hospital of Wannan Medical College from March 2020 to February 2021 were enrolled for prospective analysis. According to the difference in intervention methods, 52 cases treated with UL were included in the observation group (Obs group), while the other 52 cases treated with the same amount of normal saline were included in the control group (Con group). This experiment was approved by the ethics committee of our hospital, and all the enrolled participants signed the informed consent.Inclusion criteria were as follows: patients diagnosed with congenital heart valve disease, degenerative disease, or rheumatic valvular disease; patients who meet the indications for CVR; patients who underwent cardiac mitral valve replacement or cardiac tricuspid valve replacement under valve cardiopulmonary bypass; patients > 18 years old; and patients with New York Heart Association (NYHA) functional classification grade III-IV. Exclusion criteria were as follows: patients with drug allergy, patients with a history of other cardiac surgery, patients with multiple cardiovascular or cerebrovascular diseases, and patients with organ or immune dysfunction.CVR under cardiopulmonary bypass of all the enrolled patients was completed by the same surgical team of our hospital. Specifically, before surgery, each patient was given basic treatment such as diuresis and cardiac strengthen measures. After routine thoracotomy, 3 mg/kg heparin  Approval No.: H20030428) was injected intravenously. Cardiopulmonary bypass was established after 480 s of activated coagulation: Intubations were placed in the superior and inferior vena cava and aorta, and a catheter was inserted into the left atrium. The temperature was controlled at (30 ± 2)°C, and the superior and inferior vena cava was separated from the aorta. The aortic root was intermittently perfused with 4°C cardioplegia at an initial dose of 20 mL/kg, followed by 10 m/kg at 20-30 min intervals. The diseased valve tissue was excised through the interatrial septum, and the prosthetic valve was closed with mattress suture. Electrocardiogram (ECG) monitoring was performed after surgery to pay close attention to patients' vital signs and maintain circulatory system stability. In addition, pH values were monitored regularly, and antibiotics, polarized liquid, and other related treatments were given. Among them, in the cardioplegia injected into patients, 10,000 U/kg UL was added for the observation group, while the same amount of normal saline was added for the control group.α (TNF-α), interleukin-10 (IL-10), superoxide dismutase (SOD), and malondialdehyde (MDA) were detected via enzyme-linked immunosorbent assay (ELISA) with kits all purchased from Chongqing Zhongyuan Huiji Biotechnology Co., Ltd. Moreover, stroke volume index (SVI), pulmonary arterial wedge pressure (PAWP), and left ventricular stroke work index (LVSWI) of each patient were determined via a hemodynamic monitor .Venous blood was sampled from each patient before surgery (T0), at 8 h after surgery (T1) and at 24 h after surgery (T2). Then, the levels of creatine kinase MB (CK-MB) and cardiac troponin I (cTnI) in the samples were determined via an automatic biochemistry analyzer with kits purchased from Immunotech Co., Ltd. and Beckman Coulter (USA) Co., Ltd., respectively. In addition, the levels of tumor necrosis factor-Patients in both groups were followed up for 6 months through hospital reexamination, to record disease recurrence and evaluate their quality of life using the Medical Outcomes Study (MOS) 36-Item Short-Form Health Survey (SF-36). The MOS SF-36 is scored on a scale of 0-100, with higher scores indicating better quality of life.Markedly effective was as follows: the NYHA classification was grade I or was improved by 2 grades or more. Effective was as follows: the NYHA classification was improved by 1 grade. Ineffective was as follows: The NYHA classification was not improved or increased from grade IV to grade III: total effective rate = (the number of cases with markedly effective treatment + the number of cases with effective treatment)/total number of cases × 100%.α and IL-10 levels; oxidative stress reaction was as follows: MDA and SOD levels; hemodynamics was as follows: PAWP, LVSWI, and SVI; clinical efficacy and safety; prognosis was as follows: disease recurrence rate and SF-36 score.Cardiopulmonary bypass status was as follows: aortic crossclamp time, cardiopulmonary bypass time, and auxiliary cardiopulmonary bypass time; postoperative status was as follows: cardiac autorebeating rate, pacemaker utilization rate, electrical defibrillation rate, dopamine utilization rate, and adrenaline utilization rate; cardiac function was as follows: CK-MB and cTnI levels; inflammatory reaction was as follows: TNF-n/%) and analyzed via the chi-square test. Measurement data such as cTnl were expressed as (−χ ± s). Independent samples t-test was used for data comparison between the two groups, paired t-test was used for intragroup comparisons before and after treatment, and one-way analysis of variance (ANOVA) as well as LSD post hoc test was used for multigroup comparisons. P < 0.05 indicates a remarkable difference.This study adopted SPSS22.0 for statistical analyses. Enumeration data such as sex were expressed as , but the Obs group showed lower electrical defibrillation rate, dopamine utilization rate, and adrenaline utilization rate than the Con group   .P > 0.05); but at T1 and T2, the Obs group showed lower levels of CK-MB and cTnI than the Con group (both P < 0.05). In both groups, the levels of CK-MB and cTnI increased at T1, but decreased at T2 (both P < 0.05)  .α and IL-10 levels between the two groups at T0 (both P > 0.05); at T1 and T2, the Obs group showed a lower TNF-α level and a higher IL-10 level than the Con group (both P < 0.05). In addition, the TNF-α level of both groups increased at T1 but decreased at T2, while the IL-10 level decreased at T1 and increased at T2 (both P < 0.05) (There were no significant differences in TNF- < 0.05) .P > 0.05); at T1 and T2, the Obs group showed a lower MDA level and a higher SOD level than the Con group (both P < 0.05). In both groups, the MDA level increased at T1 but decreased at T2, while the SOD level decreased at T1 and increased at T2 (both P < 0.05)  .P > 0.05); at T1 and T2, the Obs group showed a higher LVSWI level than the Con group (both P < 0.05). In both groups, PAWP decreased with time and LVSWI increased with time, while SVI decreased at T1 and increased at T2   .P > 0.05) nor in the incidence of adverse reactions   .P > 0.05)  .MIRI is a great potential threat, as it seriously affects the patient's heart function and postoperative rehabilitation and worsens their prognosis , 19. Seaα and inhibiting MIRI induced by c-Jun N-terminal kinase (JNK) and P38 mitogen-activated protein kinase (MAPK) signaling pathways. The results of this study showed that the application of UL in has no significant effect on the clinical efficacy, safety, and prognosis of patients undergoing CVR under cardiopulmonary bypass, but it had significant protective effects on patients' cardiopulmonary bypass status, cardiac function, inflammation, oxidative stress, and hemodynamics. Evidence has shown that during the development of MIRI, the infiltration ability of neutrophils will be greatly enhanced, which mediates the aggravation of inflammatory injury of myocardial tissue and generates massive oxygen free radicals to induce the peroxidation of a large number of cells and the consequent denaturation of enzymes and ion channel proteins and DNA chain breakage, resulting in injury of myocardial tissue and myocardial function [Some researchers have analyzed the protective effect or mechanism of UL in MIRI. For example, Kawamura et al.  reportedfunction . In addifunction . Therefofunction . In addifunction , 27. UL function . In addifunction , 29. Morfunction .Through previous studies, we found that the combination of different anesthetic and sedative drugs with UL can improve the ability of UL for the management and treatment of MIRI , 32, whiUL can effectively prevent and treat MIRI after CVR under cardiopulmonary bypass and improves the cardiopulmonary bypass status, cardiac function, inflammatory response, oxidative stress response, and hemodynamics of patients, which is worthy of clinical application.


Machine learning (ML) is a rapidly advancing field with increasing utility in health care. We conducted a systematic review and critical appraisal of ML applications in vascular surgery. MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to March 1, 2021. Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a third author resolving discrepancies. All original studies reporting ML applications in vascular surgery were included. Publication trends, disease conditions, methodologies, and outcomes were summarized. Critical appraisal was conducted using the PROBAST risk-of-bias and TRIPOD reporting adherence tools. We included 212 studies from a pool of 2235 unique articles. ML techniques were used for diagnosis, prognosis, and image segmentation in carotid stenosis, aortic aneurysm/dissection, peripheral artery disease, diabetic foot ulcer, venous disease, and renal artery stenosis. The number of publications on ML in vascular surgery increased from 1 (1991–1996) to 118 (2016–2021). Most studies were retrospective and single center, with no randomized controlled trials. The median area under the receiver operating characteristic curve (AUROC) was 0.88 (range 0.61–1.00), with 79.5% [62/78] studies reporting AUROC ≥ 0.80. Out of 22 studies comparing ML techniques to existing prediction tools, clinicians, or traditional regression models, 20 performed better and 2 performed similarly. Overall, 94.8% (201/212) studies had high risk-of-bias and adherence to reporting standards was poor with a rate of 41.4%. Despite improvements over time, study quality and reporting remain inadequate. Future studies should consider standardized tools such as PROBAST and TRIPOD to improve study quality and clinical applicability. The field has been driven by the explosion of electronic data combined with increasing computational power2. ML techniques are increasingly applied to solve health care problems, with its global market value predicted to grow from $4.9 billion in 2020 to $45.2 billion by 20263. The value of ML/AI is that these technologies can automatically and quickly analyze large amounts of data to augment a clinician’s ability to diagnose disease and make predictions about outcomes, among other applications4. Compared to traditional statistical techniques, ML applies advanced computing technology to more accurately model complex relationships in large datasets5.Machine learning (ML) is a rapidly advancing field of artificial intelligence (AI) that enables computer technology to learn from data to identify patterns and make predictions without explicit programming7. Second, there are objective clinical definitions for most vascular conditions . This allows ML algorithms to automate diagnosis with little human input10. Third, vascular surgical procedures are often high-risk and performed on patients with multiple comorbidities11. Therefore, it is critical to make accurate predictions about post-operative outcomes using previous experience, which ML is designed for12. Finally, there is a growing abundance of data available to facilitate the development of ML models through the Vascular Quality Initiative, which captures patient-level data across 796 centers in North America13.Vascular surgery is highly suitable for ML applications for several reasons. First, the endovascular revolution has made vascular surgery a field that is oriented toward technology and medical imaging, facilitating the application of powerful ML-based image analysis software14, detect endoleaks15, and identify patients with PAD who have high mortality risk16. Despite an increasing amount of research interest in ML techniques, its translation to real-world practice remains limited. One reason for this could be inadequate quality or reporting of existing studies, reducing clinical applicability. Several standardized tools, including the Prediction Model Risk of Bias Assessment (PROBAST)17 and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)18, have been developed to assess the risk-of-bias and adherence to reporting standards for prediction models. Surveys of physicians demonstrate that significant barriers to the adoption of AI/ML technologies are lack of knowledge and trust in these models20. The application of standardized quality assessment tools such as PROBAST and TRIPOD can provide clinicians with more effective mechanisms to evaluate AI/ML tools and determine applicability to their practice22.ML algorithms have been applied to predict AAA growth23, plastic surgery24, and orthopedic surgery25. However, there has been no synthesis or evaluation of ML studies in vascular surgery using standardized tools such as PROBAST and TRIPOD. We conducted a systematic review and critical appraisal to comprehensively synthesize and rigorously evaluate the ML literature in vascular surgery.Given recent advances in ML technology and its potential to transform clinical practice, it is important to understand its applications to vascular surgical conditions. Systematic reviews have been conducted on ML/AI in neurosurgeryn = 1645), Embase (n = 1463), and Cochrane CENTRAL (n = 89). A total of 2235 articles remained after duplicates were removed, all of which underwent title and abstract screening. A total of 1660 records were excluded and 575 underwent full-text review. A total of 363 were excluded, most commonly because there was no ML technique (n = 286) or relevant vascular condition (n = 56). Hand-search of reference lists identified no additional articles. In all, 212 studies were included in the final systematic review and critical appraisal. Our search results are summarized in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) study flow diagram , aortic aneurysm/dissection (n = 53), PAD (n = 30), diabetic foot ulcer (n = 24), venous disease (n = 4), renal artery disease (n = 4), and other vascular conditions (n = 8). The main goals of the studies were diagnosis (n = 82), prognosis (n = 55), and image segmentation (n = 75) , China (n = 37), and UK (n = 19). A summary of included studies is presented in Supplementary Table We included 212 studies published between 1991 and 2021. The number of publications increased significantly from 1 (1991–1996) to 118 (2016–2021)  Fig. . Most stn = 120) and many did not report the data source (n = 28). Only five studies were prospectively tested, and no randomized controlled trials were conducted , particularly convolutional neural network (n = 42). Other ML models included support vector machine (n = 17), fuzzy classifier (n = 13), Bayesian model (n = 9), ensemble model (n = 8), decision tree (n = 6), and random forest (n = 6). A significant proportion of studies applied multiple ML models (n = 47)  Fig. .n = 150). A total of 41 studies used structured clinical/demographic/laboratory/genomic data alone, 16 used both structured and imaging data, 3 used clinical notes alone, and 2 used both structured variables and clinical notes as predictors.Most studies used imaging/doppler signals as the sole input feature (n = 80) followed by leave-one-out cross-validation (n = 17), while bootstrapping was less common (n = 3). External validation was performed in 9/212 (4.2%) studies.Of the studies that reported a validation method, k-fold cross-validation was the most common (n = 16). Prognostic studies included prediction models for aneurysm growth/rupture and mortality/re-intervention after surgery (n = 16). Input features were imaging alone (n = 13), structured clinical variables alone (n = 13), or a combination of structured and imaging data (n = 6). Image segmentation algorithms were designed to identify aortic true/false lumens and thrombus on computed tomography (CT) (n = 13). Sample sizes ranged from 8 to 1,049,160 with a median of 143 patients. Event rates ranged from 1.5 to 71.4% with a median of 38.4%. The area under the receiver operating characteristic curve (AUROC) ranged from 0.61 to 0.99 with a median of 0.87.Diagnostic studies focused on detection of aortic aneurysm/dissection, rupture, and endoleak (n = 22) and classification into symptomatic vs. asymptomatic status (n = 14). Prognostic studies included prediction of stenosis progression/stroke risk (n = 3), shunt necessity during endarterectomy (n = 1), and cardiovascular events following revascularization (n = 2). Input features were imaging/doppler signals alone (n = 29), structured data alone (n = 8), and a combination of structured and imaging features (n = 5). Most image segmentation algorithms were designed to identify carotid intima/media and plaque on ultrasound (n = 31). Several magnetic resonance imaging-based studies segmented carotid plaque to identify high-risk features such as ulceration, intraplaque hemorrhage, and necrotic core (n = 5). Sample sizes ranged from 10 to 90,000 with a median of 161 patients. Event rates ranged from 3.6 to 76.3% with a median of 52.6%. AUROC ranged from 0.75 to 0.99 with a median of 0.90.Diagnostic studies were focused on detecting the presence/degree of carotid stenosis (n = 10) and differentiating ischemic vs. neurogenic claudication (n = 2). Several ML models for patients with lower extremity prostheses were designed to detect falls and determine terrain type (n = 2). Prognostic studies included prediction of mortality/complications and health care utilization in patients with PAD (n = 4), ambulation potential after amputation (n = 3), and surgical site infection following lower extremity bypass (n = 1). Input features included imaging/functional data alone such as CT, ultrasound, and walking motion data (n = 13), structured clinical information (n = 9), and a combination of structured and imaging data (n = 1). Two studies used clinical notes to identify patients with PAD using natural language processing. Sample sizes ranged from 1 to 253,125 with a median of 265 patients. Event rates ranged from 12.3 to 70.0% with a median of 23.1%. AUROC ranged from 0.61 to 1.00 with a median of 0.89.Diagnostic studies were focused on detecting the presence/severity of PAD (n = 1), infection/ischemia (n = 1), and neuropathy (n = 2) using pictures and plantar pressures. Prognostic studies predicted risk of amputation (n = 2), mortality (n = 2), and ulcer healing (n = 1) based on structured clinical variables and imaging data. Image segmentation studies localized ulcers based on pictures (n = 6) and thermograms (n = 2). Two studies more specifically segmented ulcers into granulation, necrotic, or slough tissue from pictures. Sample sizes ranged from 5 to 1,567,636 with a median of 207 patients. Event rates ranged from 0.6 to 85.6% with a median of 41.2%. AUROC ranged from 0.71 to 1.00 with a median of 0.84.Diagnostic studies were focused on detection of ulceration (n = 1) and prediction of varicose vein development (n = 1) and venous ulcer development/healing (n = 2). Two studies used structured clinical data alone as input features, and two used a combination of structured and imaging data. Sample sizes ranged from 77 to 493,519 with a median of 325 patients. Event rates ranged from 1.9 to 51.9% with a median of 50.0%. AUROC ranged from 0.70 to 0.86 with a median of 0.78.ML techniques were applied to venous disease through detection of venous thromboembolism (n = 1) and captopril renography (n = 1). One study applied ML technology to identify relationships between covariates and outcomes in the Cardiovascular Outcomes in Renal Atherosclerotic Lesions trial26. Sample sizes ranged from 29 to 573 with a median of 150 patients. The median event rate was 21.2%. AUROC ranged from 0.68 to 0.93 with a median of 0.81.ML techniques were applied for detection of renal artery stenosis from completion angiogram (n = 1), prediction of cardiovascular mortality/re-admission after major vascular surgery (n = 2), identification of arteriovenous fistula stenosis (n = 1), detection of lymphedema (n = 1), and endovascular guidewire tracking (n = 2). Sample sizes ranged from 30 to 246,205 with a median of 78 patients. The event rate ranged from 7.2 to 46.2%, with a median of 30.3%. AUROC ranged from 0.68 to 0.92, with a median of 0.81.ML studies on other vascular conditions included detection and prediction of vascular injury in anterior lumbar spine surgery using clinical data and operative notes , sensitivity , specificity , accuracy . Median AUROC across included studies was 0.88 (range 0.61–1.00) and a summary of AUROC’s (medians and ranges) across each disease condition is presented in Fig. 31, 11 performed better than existing risk prediction tools such as the Glasgow Aneurysm Score, Mangled Extremity Severity Score (MESS), and Padua Prediction Score42, 1 performed better than vascular surgeons in predicting in-hospital mortality following AAA repair43, and 2 performed better than radiologists in detecting AAA on CT44. One performed similarly to logistic regression for predicting shunt necessity during carotid endarterectomy45 and another demonstrated no difference compared to radiologists in detecting aortic dissection on CT46. A summary of these findings can be found in the outcomes column of Supplementary Table Twenty-two studies compared the outcomes of their ML model to clinicians, existing risk prediction tools, or traditional regression models. Twenty performed better, two performed similarly, and none performed worse. Specifically, 6 performed better than traditional regression models such as logistic, linear, and Cox regression40, Ross (2016)16, Ravaut (2021)47, and Ross (2019)48.Of the 212 included studies, overall risk-of-bias was high for 201 (94.8%), unclear for 7 (3.3%), and low for 4 (1.9%). High risk in the analysis domain (179/212 [84.4%] studies) was the main contributor to a study being overall high risk. Specifically, many studies did not report the number of participants with missing data, perform calibration to assess model performance, or account for overfitting. In the participants' domain, 96/212 (45.3%) were high risk mainly because inclusion and exclusion criteria for their study cohort were not described. Similarly, in the outcomes domain, 101/212 (47.6%) were high risk because outcomes were not defined, blinding was not performed, and the time interval between predictor assessment and outcome determination was not reported. In the predictors' domain, some studies were high/unclear risk (82/212 [38.7%]) due to inadequate definition of predictors and unclear availability of predictor data at the time of model application. The proportion of low risk-of-bias studies increased in each domain between publication years 1991–2000 and 2011–2021: participants (40.0% vs. 45.8%), predictors (40.0% vs. 61.6%), outcomes (20.0% vs. 39.5%), and analysis (0% vs. 3.4%). Study quality improved over time, with overall low risk-of-bias studies published only after 2010 Fig. . There w40. The authors demonstrated that their ML model performed better than the existing MESS40. Ross (2016) applied decision trees to detect PAD and predict mortality using a combination of clinical, imaging, and genomic data with better predictive ability than logistic regression16. Ravaut (2021) developed an ML model from 1,567,636 patients using over 700 clinical variables from administrative health data to predict diabetes complications including amputations with an AUROC of 0.7847. Ross (2019) generated a prediction model from 7,686 patients using 1000 variables that were readily available from electronic health records including clinical data and notes to predict major adverse cardiovascular events in patients with PAD with an AUROC of 0.8148. These four studies appropriately defined their study population, predictors, and outcomes, as well as reported discrimination performance, model calibration, and supplementary data describing how readers can apply the models to their own practice.Perkins (2020) used US registry data to develop a Naïve Bayes model that predicted the risk of amputation following lower extremity revascularization and externally validated the algorithm on a UK registry with an AUROC of 0.97Overall adherence to the TRIPOD reporting checklist was 41.4%, with 19/31 domains having a rate less than 50% Fig. . Reporti49. Current ML algorithms have excellent predictive ability, with a median AUROC of 0.88. Disease conditions with more publications had higher median AUROC’s , while those with fewer publications had lower median AUROC’s . Several ML models performed better than existing clinical prediction tools (n = 11), clinicians (n = 3), and traditional regression models (n = 6). However, overall risk-of-bias was high in 94.8% of studies and adherence to reporting standards was suboptimal at 41.8%. Most studies developed ML models using retrospective, single-center data and did not report the length of follow-up. External validation was performed in less than 5% of studies. Despite improvements over time, study quality and reporting remain poor.This systematic review and critical appraisal of 212 studies published over 30 years provides a comprehensive synthesis and rigorous evaluation of the ML literature in vascular surgery. The research interest in ML has grown significantly, with a substantial increase in the number of publications between 1991 and 2021. However, some vascular conditions remain understudied, such as venous disease and renal artery stenosis, which account for less than 5% of publications. Convolutional neural networks were the most commonly applied model, reflecting the preference for advanced technology50. However, the study did not capture ML techniques in PAD, carotid stenosis, diabetic foot ulcers, venous disease, and other vascular conditions. Furthermore, quality assessment of included studies was not performed. Our systematic review captured a broader spectrum of vascular conditions and applied standardized tools (PROBAST and TRIPOD) to critically evaluate the ML literature in vascular surgery.One systematic review of AI in AAA was published in 2020, identifying 34 studies that used AI for image segmentation, diagnosis, and prognosis51. The authors demonstrated a median accuracy and AUROC of 94.5% and 0.83, respectively51. Their ML algorithms had 15% greater accuracy than logistic regression and most performed better than existing prognostic indices and clinicians51. Our study similarly demonstrated excellent predictive outcomes for ML algorithms in vascular surgery. The median AUROC of ML models in our studies was 0.88, with 6 performing better than traditional regression techniques, 11 performing better than existing risk prediction tools, and 3 performing better than clinicians. For example, Ross et al. (2016) developed an ML model with an AUROC that was 0.11 higher than logistic regression for PAD detection and mortality prediction16. Perkins et al. (2020) showed that their ML algorithm performed better than the MESS at predicting outcomes following lower extremity revascularization for trauma patients (AUROC 0.97 vs. 0.74)40. Talebi et al. (2020) compared their ML model to generalist radiologists for the detection of AAA from CT and demonstrated an accuracy that was 5–25% higher15.The predictive potential of ML has been demonstrated in other surgical specialities. For example, Senders et al. (2018) conducted a systematic review of 34 publications on ML models for outcome prediction in neurosurgery52. They demonstrated that overall risk-of-bias was high in 58/81 (71.6%) studies based on PROBAST criteria and there was <50% adherence to 12 TRIPOD items52. Similarly, Wynants et al. (2020) assessed 169 prediction models for COVID-19 diagnosis/prognosis and determined that overall risk-of bias was high or unclear for all of them53. We also demonstrated a high risk-of-bias and poor adherence to reporting standards for ML studies in vascular surgery.Previous groups have analyzed the risk-of-bias and adherence to reporting standards for ML studies. Nagendran et al. (2020) conducted a systematic review evaluating the outcomes of deep learning prediction models versus clinicians54. Most studies on ML models in vascular surgery have been published in the past 5 years. Furthermore, standardized guidelines on the conduct of ML studies have not been widely adopted55. These reasons likely explain the suboptimal quality and reporting of current studies. We demonstrate improvements in study quality and adherence to reporting standards over time, suggesting that higher research quality coincides with the development of the field. Furthermore, our data suggest that model performance improves with the increasing application of ML techniques to vascular surgery, particularly in aortic, carotid, and PAD. We also identified disease conditions that require greater attention including diabetic foot ulcers, venous disease, and renal artery stenosis.ML has gained tremendous interest in recent years but remains a relatively novel field, particularly with respect to health care applications56. However, few ML studies in vascular surgery have leveraged this technological advantage, with many using solely structured or unstructured data as predictors. Ross et al. (2019) used a combination of unstructured text data from clinical notes and structured information from diagnostic/procedural codes, prescriptions, vital signs, and laboratory investigations to predict major adverse cardiac and cerebrovascular events in PAD patients48. The authors performed a sensitivity analysis demonstrating that the removal of text data decreased model performance from an AUROC of 0.81–0.78 (p = 0.002)48. Following the example set by Ross et al.48, future studies should consider training ML models on multiple data types to potentially increase predictive power.ML has significant advantages over traditional risk prediction tools as they can learn from a wide range of data types, including structured clinical, laboratory, and genetic information along with unstructured imaging data and clinical notes57. However, most studies did not describe how the reader could apply their algorithm nor provide source code. This makes it challenging to test the model in different clinical settings and build on existing algorithms. To improve clinical applicability and accelerate the advancement of the field, future studies should consider publishing their de-identified raw data and source codes through repositories such as GitHub58.A distinguishing feature of ML models is their ability to learn continuously to improve performance40. The authors demonstrated that their algorithm maintained excellent performance in their external validation population, with an AUROC of 0.9740. Perkins et al. then developed an internationally accessible website for clinicians to apply their model (https://www.traumamodels.com/). Future work on ML in vascular surgery should follow this example in developing generalizable, accessible, and clinically relevant tools.Given the novelty of the field, most ML studies have been developed and tested on retrospective data. There are currently no randomized controlled trials assessing the impact of this technology on vascular surgical outcomes. Furthermore, few studies externally validated their algorithm. It will be critical for future studies to assess the impact of ML models on clinically relevant outcomes and their ability to function in different clinical settings. For example, Perkins et al. (2020) developed an ML algorithm to predict outcomes following lower extremity revascularization in trauma patients using data from the US Joint Trauma System and externally validated their model on the UK Joint Theatre Trauma Registry59. Less than 20% of studies in our systematic review reported inclusion/exclusion criteria and demographic characteristics for their study population. This poses a significant risk of prediction tools disadvantaging minority populations. Future work should ensure that their study cohort captures an appropriately diverse population.It is also essential for ML models to consider biases including gender, racial, and socioeconomic disparities40, Ross (2016)16, Ravaut (2021)47, and Ross (2019)48 are four studies judged to be at low risk-of-bias with good potential for broad clinical implementation in vascular surgery. These papers provided a detailed description of their study population with inclusion and exclusion criteria, reported specific definitions for their variables and outcomes of interest, identified the specific timepoint during a patient’s clinical course when their algorithm should be applied, and assessed model performance using various calibration methods48. Future work should look toward these publications for guidance on study methodology and consider building on their algorithms.Perkins (2020)63. Generally, the first consideration is devising a specific, clinically relevant question with input from end-users60. Then, it is critical to build a team of clinicians, computer scientists, and administrators with expertise in patient care and model development60. Together, this group can assess whether there is sufficient quantity and quality of data available to develop a model that can adequately address the problem of interest60. Given that overfitting can be a significant problem, it is recommended to create simple models without an abundance of extraneous features that do not contribute to predictive performance61. A multidisciplinary team can provide guidance on selecting important input variables to inform an accurate model61. Furthermore, evaluating the generalizability of the algorithm and its associated biases is essential prior to clinical implementation62. Importantly, the model’s impact on patient outcomes and clinician workflow should be prospectively evaluated, particularly in vascular surgery where patients often undergo high-risk, urgent interventions62. Finally, post-implementation evaluation with regular performance monitoring and system retraining with up-to-date information is important given the constant evolution of clinical practice and datasets62.Developing and implementing successful ML models in vascular surgery requires a detailed and systematic approach, which has been described by others53. Currently, work is underway to develop a TRIPOD-ML tool64. Furthermore, there may be publication bias, with high-performing ML models being more likely to be published.This study has several limitations. PROBAST and TRIPOD are designed to assess prediction models, but have not been validated specifically for ML applications. However, previous studies have used PROBAST and TRIPOD to evaluate ML models as quality assessment criteria for clinical prediction tools and ML algorithms are similarOur systematic review and critical appraisal of 212 studies demonstrates that ML models have excellent predictive power in vascular surgery within the research setting with a median AUROC of 0.88. Many models performed better than traditional regression techniques, existing prediction tools, and clinicians. ML technology can provide powerful augmentation to clinicians for image analysis, disease diagnosis, and outcome prediction. However, risk-of-bias and adherence to reporting guidelines are currently substandard, likely due to the novelty of the field. Given the need for ML algorithms to be rigorously validated prior to clinical implementation, future studies should strongly consider standardized tools such as PROBAST and TRIPOD to guide study design and reporting.66. Our study protocol (CRD42021240310) was registered with the International Prospective Register of Systematic Reviews67. Ethics approval was not required for this study as this was a systematic review of published articles.A systematic review was conducted according to the PRISMA statement guidelines68. We hand-searched the reference lists of included studies for additional relevant articles. Our search did not apply language limitations and Google Translate was used for non-English studies69. The search strategy is detailed in Supplementary Table Our search strategy was devised in consultation with an experienced librarian (TK). MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL) were searched from inception to March 1, 2021, for studies reporting ML applications in vascular surgery. A combination of Medical Subject Heading terms, keywords, and synonyms for ML AND vascular surgery were used to maximize sensitivity. EndNote Version 20 was used to collate references70. We included all original studies reporting ML applications in vascular surgery, including case reports, case series, observational studies, and clinical trials. Reviews, commentaries/editorials/letters, animal studies, and articles without full text were excluded.Title and abstract screening, full-text review, data collection, and assessment of risk-of-bias and reporting adherence were conducted by two independent reviewers (BL and TF), with a third author resolving discrepancies (CC-O). Covidence was used to facilitate the systematic review71. Variables obtained were study authors, publication year, country, data collection period, disease condition, study objective , study design, data source, ML model, input features, prediction outputs, sample size, training/validation/test sets, validation method, reporting of external validation, follow-up, and outcomes. Authors were contacted through email for relevant information not reported in the original publication.A standardized form was used to collect data for included studies based on the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies Checklist72.Publications trends were assessed by plotting the number of included studies in 5-year intervals between the first and last published articles (1991–2021). Bar graphs were developed to summarize the number of papers focused on the different vascular conditions, main goal (diagnosis/prognosis/segmentation), ML model applied, and study design. Study outcomes including AUROC, sensitivity, specificity, and accuracy were summarized as medians and/or ranges across included articles and percentage of studies reporting values ≥80%. This threshold represents the excellent discriminatory ability of a prediction model17. PROBAST assesses four domains  with 20 study methodology questions to determine overall risk-of-bias17. Reporting adherence was assessed using the TRIPOD tool18. TRIPOD is a 31-item checklist that provides reporting standards for prediction model studies18. Trends over time for included studies based on PROBAST risk-of-bias and TRIPOD adherence was assessed in 10-year intervals based on publication year .Critical appraisal was performed by assessing the risk-of-bias and adherence to reporting standards for individual articles and collating the results to determine the overall quality of included studies. Specifically, risk-of-bias was assessed using the PROBASTAll numerical analyses were conducted using R version 4.0.3 .Supplementary Information


Excessive external femoral rotation (FR) can functionally increase stem anteversion (SA) and is often observed at an early stage after surgery in revision total hip arthroplasty (THA). This study was conducted to investigate the prevalence of external FR, identify the factors associated with external FR, and determine the association of FR and other factors with hip dislocation in revision THA.We enrolled 51 revision THA patients (55 hip cases). The patient background, angle of anatomical and functional SA, FR angle, sizes and densities of muscles around the hip joint, impingement distance, and consequence of postoperative hip dislocation were assessed by reviewing their medical history and imaging data that includes computed tomography (CT) scans before and after surgery.r =  − 0.54) and increase in functional SA (r = 0.36), which was significantly correlated with impingement distance (r = 0.46). The independent factors associated with external FR in multivariate analysis were the anatomical SA, CT densities of the psoas, gluteus medius and maximus muscles, and 2-stage revision (R2 = 0.559). During follow-up period, eight cases of revision THA showed hip dislocation. FR, functional SA, impingement distance, CT density of psoas and gluteus medius muscle, body mass index, number of past operation, and ratio of 2-stage revision THA were significantly different between cases with dislocation and non-dislocation. The odds ratio of FR and impingement distance for hip dislocation was identified as 1.061: 1.011–1.114) and 0.901 (95% CI 0.820–0.991), respectively.Forty-five hip cases (81.8%) showed external FR (mean 13.0°). External FR was significantly correlated with anatomical SA (Revision THA frequently causes an external FR that functionally increases the SA and impingement risk, particularly in hips with 2-stage revision with psoas and gluteus medius muscle atrophy. Patients who have undergone revision THA and have an excessive external FR may require careful monitoring for possible hip dislocation due to hip joint instability and impingement. Using the combined anteversion technique for the cup and stem anteversion (SA) angle can minimize the risk of hip impingement during daily hip motions in total hip arthroplasty (THA) or revision THA patients –4. RecenThe muscles around the hip joint affect its stability, which is an important factor in preventing postoperative hip dislocation in THA patients –12. The Therefore, this study was conducted to investigate the prevalence of external FR in revision THA patients, identify the factors associated with external FR, and determine the association of FR and the other factors with the risk of hip dislocation in revision THA.2) . The records of 123 patients who underwent revision THA in Yokohama City University Hospital from 2006 to 2019 were reviewed. The exclusion criteria were as follows: replacement of the head or liner only, uncontrollable periprosthetic joint infection (PJI), did not provide consent to participate, no follow-up after 6 months, and failure to undergo X-ray and computed tomography (CT) scans both before and after revision THA . P values < 0.05 were considered to indicate statistically significant results. All graphs were prepared using GraphPad Prism 8 software .Statistical significance was determined using Student’s Among the 51 patients with 55 hips who underwent revision THA, 45 hip cases (81.8%) had external FR with a mean angle of 13.0° [standard deviation ± 12.6°] postoperatively, including 14 hip cases (25.5%) with a more than 30° external FR with a maximum of 56° in revision THA . The significant correlation between the external rotation of the femur and functional SA (r = 0.36) indicated the importance of the FR angle on functional SA   Table :1\documePostoperative hip dislocation occurred in 8 hips during the follow-up period. Although the anatomical SA was not significantly different between revision THA patients with dislocation and non-dislocation, external FR and functional SA were significantly high in patients with dislocation Table . The othOperative planning to achieve an optimal combined cup and SA is important for successful THA –3. PreviAbductor deficiency contributes to hip instability –15, 20, Multiple regression analysis revealed the independent functions of the psoas and Gmed muscles on the internal FR. Nevertheless, the psoas and Gmed muscles reportedly contribute to hip stability , 13–15. Our study had several limitations. First, more than 50% of the revision THA patients treated at our hospital were not included in retrospective evaluations because of a lack of medical records, clinical follow-up, and image assessment. However, we included all dislocation cases during the study period that may have resulted in successful analysis of the association of FR with dislocation. Secondly, we did not evaluate mid- and long-term changes in FR in our study cases. However, our study is the first to demonstrate the tendency towards an external FR, and to reveal an association between the lower muscle CT density and early postoperative posture in a revision THA cohort. SA modification or muscle strengthening may be required in these patients; however, further studies are needed to validate this possibility.In conclusion, more than 80% of revision THA caused an external FR that functionally increased the SA and impingement risk in leg extension, particularly in hips with 2-stage revision with psoas and Gmed muscle atrophy. Revision THA patients with an excessive external FR may require careful monitoring for hip dislocation due to instability and impingement.


This study aimed to compare the short-term outcomes of transcatheter and surgical aortic valve replacements (TAVR and SAVR) in high-, intermediate-, and low-preoperative risk patients.A total of 454 patients who underwent TAVR or SAVR were included. Patients were categorized into high-, intermediate-, and low-risk according to the Society of Thoracic Surgery-Predicted Risk of Mortality score and clinical outcomes were compared between TAVR and SAVR groups.p < 0.001), less frequent new-onset atrial fibrillation (p < 0.001), and shorter intensive care unit stay (p < 0.001). Furthermore, transcatheter valves performed better than surgical valves, with lower peak velocity (p = 0.003) and pressure gradient (p < 0.001) and higher effective orifice area index (p < 0.001). The clinical outcomes of TAVR were comparable to or even superior to those of SAVR in high- and intermediate-risk patients. In low-risk patients, the 1- and 2-year mortality rates were 6.3% and 12.1%, respectively, in the TAVR group and 0% and 0.9%, respectively, in the SAVR group (p < 0.001). Mild or greater paravalvular leakage was a risk factor for mortality .TAVR was less invasive, with less bleeding and transfusion (TAVR was superior to SAVR in the sense of less invasiveness and valvular function. However, the indication of TAVR in low-risk patients should be carefully discussed, because paravalvular leakage was a risk factor for short-term mortality.The online version contains supplementary material available at 10.1007/s11748-021-01672-8. Surgical aortic valve replacement (SAVR) has long been the gold standard for the surgical treatment of severe aortic stenosis (AS). Over the last decade, transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of severe AS. Early studies showed a clear benefit of TAVR in prohibitive and high-surgical-risk patients , –3 and inThe results of RCTs have often been criticized because of the limited comparability to “real-world” practice. The surgical community has further questioned these because of the potential conflicts of interest and the perceived “suboptimal” outcomes for SAVR –13. HoweEthics approval was obtained from the appropriate ethics committee for this retrospective study. From October 2015 to July 2019, 678 patients underwent aortic valve replacement (AVR) at our institutes. Of the 678 patients, 224 underwent AVR concomitantly with aortic or mitral surgery, left ventricular restoration, left ventricular assist device implantation, or AVR for aortic insufficiency or infective endocarditis and were excluded from this study . The remaining 454 patients who underwent AVR for severe AS were then included in this study. Patients were divided into the TAVR and SAVR groups, and clinical outcomes were compared. For the mortality and morbidity analyses, patients were categorized into 3 groups according to preoperative STS-PROM score: STS-PROM ≥ 8.0 (high-risk group), 4.0 ≤ STS-PROM < 8.0 (intermediate-risk group), and STS-PROM < 4.0 (low-risk group) . Mortality and morbidity were compared between the TAVR and SAVR groups.The selection of treatment (TAVR or SAVR) has always been performed through discussions within the heart team, consisted of cardiologists, surgeons, anesthetists, nurses, and clinical engineers. Transcatheter approach or open surgical approach was chosen considering the patient’s age, severity of condition, frailty, cognitive function, and comorbidities. All the procedures were done under general anesthesia and with the use of transesophageal echocardiography. The approach in TAVR group  were chosen through discussions within the heart team considering the anatomical characteristics of the patient. SAVR was done through median sternotomy and under cardiac arrest with cardiopulmonary bypass support.Data were extracted from patient charts recorded in the hospital’s computer database. Follow-up started on the day of TAVR or SAVR and was censored at 30 months. Postoperative echocardiographic study was done one week after the procedure in most of the patients. In some patients in TAVR group, echocardiography was also done 1 day after the procedure to rule out complications such as cardiac tamponade. The first postoperative echocardiographic data was used for the analysis.t test, non-normally distributed variables were compared using the Mann–Whitney U test, categorical variables were compared using Chi-squared analysis or Fisher’s exact test, as appropriate. Time-to-event analyses were performed using Kaplan–Meier estimates and compared with the use of the log-rank test. The predictors of mortality were evaluated using the Cox hazard model. Factors with a value of p < 0.10 in the univariate analysis were included in the multivariable analysis.Continuous variables are presented as mean ± SD, and categorical variables are presented as numbers and proportions. All continuous variables were checked for normal distribution using the Shapiro–Wilk test and a normal probability plot. For univariate analyses, normally distributed variables were compared using Student’s In the low-risk group, the comparison between TAVR and SAVR was further done using propensity score matching. From the data of 66 TAVR patients and 121 SAVR patients, a multivariable logistic regression model was used to develop propensity score for the selection of TAVR with seven variables from baseline characteristics that were significantly different between TAVR and SAVR groups. The c statistics was 0.937.p values less than 0.05 were considered to be statistically significant.All two-sided All statistical analyses were performed using EZR , a graphical user interface for R . More precisely, it is a modified version of R commander designed to add statistical functions frequently used in biostatistics .p < 0.001). There were more female patients in the TAVR group , while the body-mass index was lower in the TAVR group . Furthermore, coronary artery diseases and peripheral vascular diseases were more prevalent in the SAVR group . In contrast, the TAVR group tended to have cerebral vascular disease or carotid artery disease more frequently (p = 0.073). As TAVR is still prohibited in hemodialysis patients in Japan, the proportion of patients with renal dysfunction and on hemodialysis was higher in the SAVR group (p < 0.001). Bicuspid aortic valve was significantly more prevalent in the SAVR group (p < 0.001). The preoperative mean left ventricular ejection fraction was higher in the TAVR group (58.3 ± 11.2%) than in the SAVR group . Emergency and urgent operations were more frequent in the SAVR group (p < 0.001), with concomitant or serial coronary revascularization being performed more frequently in the SAVR group (p < 0.001). The prostheses used in the TAVR and SAVR groups are summarized in Supplemental Table 1.The number of patients in each group and the proportion who underwent TAVR/SAVR are summarized in Supplemental Fig. 1. The characteristics of the TAVR and SAVR patients at baseline are summarized in Table p = 0.007). The SAVR group also experienced more intraoperative bleeding (p < 0.001), requiring more transfusions (p < 0.001). New-onset atrial fibrillation was observed in 29.6% of the SAVR patients and only 5.9% of the TAVR patients (p < 0.001). The majority of TAVR patients (77.7%) were extubated in the operating room, while none of the SAVR patients were extubated (p < 0.001). Intensive care unit stay was significantly longer in the SAVR group than in TAVR group . On the other hand, the TAVR group (9.2%) required more postoperative permanent pacemaker implantation than the SAVR patients . Peripheral vascular complications were observed only in TAVR patients .The operative outcomes of the TAVR and SAVR groups are summarized in Table p = 0.003). Similarly, the mean and peak pressure gradients were also significantly higher in surgical valves  than in transcatheter valves . Meanwhile, the effective orifice area index was higher in transcatheter valves (1.27 ± 0.35 cm2/m2) than in surgical valves . Patient-prosthesis mismatch (PPM) occurred more frequently in the SAVR group than in the TAVR group. Mild or more PPM (effective orifice area index < 0.85 cm2/m2) was observed in 20.4% of the SAVR group compared to 8.0% of the TAVR group (p < 0.001). Severe PPM (effective orifice area index < 0.65 cm2/m2) also tended to be seen more frequently in the SAVR group . In contrast, paravalvular leakage (PVL) was significantly more frequent in the TAVR group . Mild or greater PVL was also seen more frequently in the TAVR group (20.6%) than in the SAVR group .The postoperative echocardiographic findings in the TAVR and SAVR groups are summarized in Table p = 0.044), with the STS-PROM being significantly higher in SAVR (16.8 ± 8.2) than in TAVR . Furthermore, there were more patients classified as New York Heart Association functional class III or IV in the SAVR group (60.3%) than in the TAVR group .The baseline characteristics of the high-risk group patients are summarized in Supplemental Table 2-1A. The differences in the baseline characteristics between the TAVR and SAVR groups were similar to the overall patients. In brief, the patients in the TAVR group were older and more likely to be female patients. On the other hand, the SAVR group was associated with more coronary artery disease, peripheral vascular disease, renal dysfunction, emergent/urgent operations, and coronary procedures. Systolic heart function was significantly more impaired in the SAVR group  , intraoperative bleeding , red blood cell transfusion , newly induced renal replacement therapy , and length of intensive care unit stay  were independent predictors of mortality .Among the preoperative factors, multivariable analysis using the Cox hazard model revealed that none of the factors, including operative procedures (TAVR or SAVR), were independent predictors of mortality . Among the postoperative factors, the requirement of extracorporeal membrane oxygenation (ECMO) support . The frequency of rehospitalization did not differ between the groups (p = 0.172), and the combined outcome of death, stroke, or rehospitalization was also not significantly different between the groups (p = 0.192) . The other clinical outcomes in the TAVR and SAVR groups are summarized in Supplemental Table 2-2B.There was no difference in all-cause mortality between the TAVR and SAVR groups Fig. B. The inp = 0.046) and prosthetic valve endocarditis  were independent predictors of mortality .Among the preoperative factors, multivariable analysis using the Cox hazard model revealed that none of the factors, including operative procedures (TAVR or SAVR), were independent predictors of mortality . Among the postoperative factors, the requirement of ECMO support  and had more female patients , there was no significant difference in the prevalence of other comorbidities, except for cerebral vascular disease or carotid disease . Therefore, due to the higher age in the TAVR group, STS-PROM was higher in this group . Furthermore, the left ventricular ejection fraction was higher in the TAVR group (60.7 ± 9.5%) than in the SAVR group  .The baseline characteristics of the low-risk group are summarized in Supplemental Table 2-3A. The differences in the baseline characteristics between the TAVR and SAVR groups were somewhat different from those of the other two categories. Although the TAVR group had a higher mean age . There was no significant difference in the frequency of stroke or rehospitalization, or the combined outcome of death, stroke, or rehospitalization between the groups . The other clinical outcomes in the TAVR and SAVR groups are summarized in Supplemental Table 2-3B.No mortality occurred in either group for up to 4 months Fig. C. The alp = 0.031) . Among the postoperative factors, prosthetic valvular endocarditis  and mild or greater paravalvular leakage  were predictors of mortality . The cause of death in in the low-risk group, along with the severity of postoperative paravalvular leakage, is listed in Supplemental Table 3.Among the preoperative factors, the multivariable analysis using the Cox hazard model revealed that only the operative procedure (TAVR) was the predictor of mortality .The baseline characteristics of the low-risk group after the propensity score matching are summarized in Supplemental Table 4. There was no difference in the baseline characteristics between TAVR and SAVR groups. The all-cause mortality at 1 and 2 years was 4.6% and 21.4%, respectively, in the TAVR group and 0% and 0%, respectively, in the SAVR group . The difference was statistically significant Supplementary file2 (DOCX 73 KB)Supplementary file3 (DOCX 18 KB)Supplementary file4 (DOCX 25 KB)Supplementary file5 (PPTX 49 KB)Supplementary file6 (PPTX 70 KB)Supplementary file7 (PPTX 72 KB)Supplementary file8 (PPTX 70 KB)Supplementary file9 (PPTX 41 KB)Supplementary file10 (DOCX 14 KB)Below is the link to the electronic supplementary material.


The skeletal muscle index (SMI), which is calculated as the ratio of skeletal muscle area at the third lumbar vertebral level divided by height squared, has been considered a prognostic factor in patients with breast cancer. However, the prognostic impact of changes in SMI during treatment remains unclear. This study aimed to evaluate the influence of SMI changes in patients with breast cancer undergoing neoadjuvant chemotherapy (NAC).We reviewed patients with breast cancer who underwent NAC and subsequent surgery for breast cancer between 2010 and 2017. The rate of SMI change during NAC was calculated, and the association between SMI changes and prognosis was retrospectively analyzed.p <  0.001 for the decreased vs. increased SMI groups; HR 3.49, p <  0.001 for the decreased vs. maintained SMI groups). Moreover, decreased SMI was an independent risk factor for disease-free survival in multivariate analysis .In total, 141 patients were evaluated. 48 (34.0%), 53 (37.6%), and 40 (28.4%) patients exhibited increased (≥ 3%), maintained , and decreased (− 3% ≥) SMI during NAC, respectively. The decreased SMI group showed significantly poorer disease-free survival than the maintained and increased SMI groups (hazard ratio [HR] 8.29, Skeletal muscle loss during NAC predicts poor prognosis. Our results underscore the importance of monitoring and maintaining skeletal muscle mass during NAC.The online version contains supplementary material available at 10.1186/s12885-022-09443-1. We previously demonstrated that a decrease in the prognostic nutritional index (PNI), which represents systemic nutritional and immunological status, during NAC had a prognostic impact in patients with breast cancer [Neoadjuvant chemotherapy (NAC) has become a standard treatment option for patients with locally advanced but operable breast cancer . NAC cant cancer . Hence, The influence of body weight and composition on patient outcomes has been a focus in the field of breast cancer studies. Previous studies have indicated that high body mass index (BMI) was associated with poor prognosis in patients with breast cancer , 5. ConvSarcopenia, defined as a condition with loss of muscle mass in older adults , is corrThis study aimed to investigate the prognostic impact of changes in skeletal muscle mass during NAC in patients with breast cancer. To this end, we evaluated the changes in SMI during NAC in patients and investigated their association with patient outcomes.18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG-PET/CT) within both 4 weeks of initiation of NAC and surgery following NAC. Patients who could not complete NAC owing to chemotoxicity or disease progression during NAC (n = 14) were excluded. A total of 141 patients were included in this study. This study conformed to the provisions of the Declaration of Helsinki . The study was approved by the local ethics committee on the clinical investigation of Shinshu University (no. 5037). Because this was a retrospective study of anonymized data, the need for informed consent was waived.This retrospective study assessed patients with breast cancer with a performance status (Eastern Cooperative Oncology Group performance status ) of 0 wh18F-FDG on PET/CT findings was apparent. Disease-free survival (DFS) was defined as the time from surgery to the date of detection of locoregional relapse or distant metastases, whichever occurred first. Overall survival (OS) was assessed from the day of surgery to the date of death from any causes.Clinical information, including age, height, body weight, sex, menopausal status, clinical stage at diagnosis, histological type, histological grade (HG), estrogen receptor (ER) status, progesterone receptor (PgR) status, HER2 status, lymph node metastasis, NAC regimens, surgical procedure, pathological responses to NAC, and presence of recurrence, were collected from the patients’ medical records. ER, PgR, and HER2 statuses were examined in samples collected by pretreatment core needle biopsy. Subtypes of breast cancer were defined as follows: luminal (ER or PgR positive and HER2 negative), luminal HER2 (ER or PgR positive and HER2 positive), HER2-enriched , and TN . HG was determined according to the Scarff–Bloom–Richardson grading system. Lymph node metastasis was determined by fine-needle aspiration cytology (FNA), but some patients were considered positive for lymph node metastasis without FNA if abnormal swelling of lymph nodes in CT findings or strong uptake of 18F-FDG-PET/CT images before NAC and surgery. 18F-FDG PET/CT scans were taken at the Ichinose Neurosurgical Hospital  with a standard technique using a Discovery ST Elite Performance scanner . Attenuation-corrected images were reconstructed in the coronal plane. SMA was measured as the cross-sectional area of the surrounding muscles  by semi-automatic tracing using images at the third lumbar vertebral level (L3) visualized within a range of − 29 to 150 Hounsfield units using the EV Insite R  system and expressed in cm2. SMI was calculated as the ratio of SMA divided by height squared (m2), whereas BMI was calculated as the patient’s body weight (kg) divided by height squared (m2). The percent change in SMI and BMI was calculated as the percent change in each parameter after NAC from that at pretreatment. The serum albumin (Alb) (g/dl) and neutrophil-to-lymphocyte ratio (NLR), which was calculated as the total neutrophil count divided by the total lymphocyte counts, were also obtained from pre- and post-NAC blood examination. The change of Alb and NLR value during NAC were calculated as each value on post-NAC minus that on pre-NAC.Skeletal muscle area (SMA) was measured using CT or 2 fluorouracil, 100 mg/m2 epirubicin, and 500 mg/m2 cyclophosphamide) regimen, except for one patient who had been treated with an EC (60 mg/m2 epirubicin and 600 mg/m2 cyclophosphamide) regimen in another hospital and was transferred to our hospital for surgery, and (2) taxane regimens, including triweekly administered docetaxel (DTX) 75 mg/m2 or weekly administered paclitaxel (PTX) 80 mg/m2. Four cycles of DTX or PTX was administered following four cycles of EC/FEC. In HER2-positive patients, 6 mg/kg (triweekly) or 2 mg/kg (weekly) trastuzumab was administered simultaneously with a taxane regimen. For the EC/FEC regimen, dexamethasone (12 mg) and granisetron hydrochloride (3 mg) or palonosetron hydrochloride (0.75 mg) were infused intravenously before the administration of chemotherapeutic agents. Together with these drugs, aprepitant (125 mg) was orally administered to avoid chemotherapy-induced nausea and vomiting since 2011. Concomitantly, dexamethasone (8 mg) was administered orally one and 2 days after treatment. In addition, aprepitant (80 mg) or granisetron hydrochloride (2 mg) was orally administered from 1 day after treatment for two (aprepitant) or five (granisetron hydrochloride) consecutive days. For PTX and DTX treatment, 8 mg of dexamethasone was administered intravenously prior to treatment. In addition, for patients treated with DTX, 8 mg of dexamethasone was administered 1 day before treatment and 1 day and 2 days after treatment  orally. Surgery was performed within 4–7 weeks after NAC completion. All patients underwent axillary lymph node dissection. The efficacy of NAC was pathologically examined in surgical specimens, and pCR was defined as no evidence of residual invasive carcinoma in the breast tissue, regardless of the axillary lymph node status.Two different NAC regimens were used: (1) triweekly administered anthracycline-based fluorouracil-epirubicin-cyclophosphamide (FEC)  was administered every 3 weeks for 12 months to patients with HER2-positive breast cancer. Whole breast irradiation of 50–60 Gy was performed for patients who underwent breast-conserving surgery, whereas chest wall and regional lymph node irradiation of 50–60 Gy was performed for patients with more than three nodal metastases on postoperative pathological examinations or preoperative imaging examinations, including ultrasonography, magnetic resonance imaging, or t-tests or one-way analysis of variance with Tukey’s multiple comparisons. Survival curves were estimated using the Kaplan–Meier method, and significant differences in survival were assessed using the log-rank test. Univariate and multivariate analyses with a Cox proportional hazards model were performed to determine the significant factors associated with OS. Multivariate analysis was performed for parameters with p <  0.05 in the univariate analysis. All statistical analyses were performed using StatFlex version 6  and GraphPad Prism 8.0.2 , and p < 0.05 was considered statistically significant.Categorical variables were analyzed using the chi-squared test, whereas continuous variables were analyzed using two-sided : Fig. S1a, b). On the other hand, the percent change in SMI showed a tendency to have a positive correlation with the percent change in SMI . The median follow-up period after surgery was 70  months, and 33 (23.4%) patients developed recurrence. Among these recurrences, locoregional relapses  were observed in 5 (3.5%) patients, while distant metastases  occurred in 28 (19.9%) patients.The clinicopathological characteristics of the 141 patients enrolled in this study are shown in Table 1p = 0.91 for pre-SMI, p = 0.70 for pre-BMI) . In contrast, after NAC, patients who developed recurrence exhibited significantly lower post-SMI (p = 0.010), whereas post-BMI did not show a difference between the two groups (p = 0.88) .To analyze the influence of skeletal muscle mass and body weight on disease recurrence, we divided patients into two groups according to the presence of recurrence and compared pre- and post-SMI and BMI. There were no significant differences in either pre-SMI or pre-BMI between patients with recurrence and those without recurrence (p = 0.03) and higher pCR rate (p = 0.03) than the maintained and decreased SMI groups. Recurrence was significantly more prevalent in the decreased SMI group than in the maintained and decreased SMI groups (p = 0.04). Distant metastases were frequently developed in the decreased SMI group (45.0%) compared to the maintained (13.2%) and decreased (6.3%) SMI groups although there was no significant difference (p = 0.32). As shown in Table As we found a significant difference in post-SMI between patients with and without recurrence after NAC, we focused on the changes in SMI during NAC in individual patients and their correlation with DFS and OS. To address this, we divided the patients into three groups  according to the percent change in SMI. As shown in Fig. p < 0.001 for the decreased vs. increased SMI group; HR 3.49, 95% CI 1.65–7.36, p < 0.001 for the decreased vs. maintained SMI group)   . With regard to subtype, the decreased SMI group showed significantly worse DFS than the increased SMI group in patients with luminal (p < 0.001), HER2-enriched (p = 0.020), and TN (p = 0.039) breast cancer. Of the 30 patients with luminal HER2 breast cancer, only two developed recurrence. Possibly due to the lack of patients who had recurrence, we did not find statistical significance in DFS between the decreased and increased groups in this subtype. However, one patient who exhibited disease recurrence was in the decreased group .To investigate whether the effect of SMI change on prognosis is dependent on various clinical factors, including menopausal status , pretreatment clinical stage (stage II vs. stage III), and subtype of breast cancer, we divided the patients according to these factors and compared the DFS among the decreased, maintained, and increased SMI groups with each factor. We found that the decreased SMI group showed poorer DFS than the maintained and increased SMI groups regardless of menopausal status and clinical stage  . Furthermore, irrespective of taxane regimen, the decreased SMI group exhibited poorer DFS than the maintained and increased SMI groups .Next, we investigated whether the kind of taxane regimen (PTX or DTX) would affect the prognosis or impact of SMI change, and found that there was no significant difference in DFS between the patients treated with PTX and those treated with DTX . The other factors that correlated with poorer DFS were postmenopausal , luminal HER2 , pre-NAC clinical stage III , and pCR . On multivariate analysis using the Cox hazards model, SMI decrease was an independent predictive factor for poorer DFS   have demonstrated that pharmacotherapies with anamorelin (a ghrelin receptor agonist) significantly increased lean body mass in patients with non-small cell lung cancer who underwent chemotherapy . HoweverThis study has some limitations. First, it was a retrospective analysis with a relatively small study population in a single institution from one Asian country. Second, the NAC regimens were not uniform among patients because the study period spanned several years when the treatment regimens were changed. Furthermore, majority of patients enrolled in this study had luminal subtype breast cancer and were perimenopausal. Thus, estrogen-related effect can have some impact on the results of this study. Although our results indicate that a decrease in SMI is associated with poor outcomes irrespective of menopausal status and subtype, we could not determine the impact of estrogen-driven factors from our results. Chemotherapy-induced menopause also should be taken into consideration. To overcome these limitations and validate our results, further investigations are required. In particular, a prospective multicountry study is warranted to diversify our findings to more heterogenous populations with different races/ethnicities and environmental backgrounds.The findings of the present study indicate that skeletal muscle loss during NAC can be a prognostic marker in patients with breast cancer. If this observation is confirmed in larger prospective studies from other countries, it could provide the basis of the importance to monitor and maintain skeletal muscle mass during NAC.Additional file 1.Additional file 2.Additional file 3.Additional file 4.Additional file 5.Additional file 6.Additional file 7.


To investigate the value of using specific region of interest (ROI) on contrast-enhanced CT for differentiating renal angiomyolipoma without visible fat (AML.wovf) from small clear cell renal cell carcinoma (ccRCC).Four-phase  contrast-enhanced CT images of AML.wovf (n = 31) and ccRCC (n = 74) confirmed by histopathology were retrospectively analyzed. The CT attenuation value of tumor (AVT), net enhancement value (NEV), relative enhancement ratio (RER), heterogeneous degree of tumor (HDT) and standardized heterogeneous ratio (SHR) were obtained by using different ROIs , and the differences of these quantitative data between AML.wovf and ccRCC were statistically analyzed. Multivariate regression was used to screen the main factors for differentiation in each scanning phase, and the prediction models were established and evaluated.Among the quantitative parameters determined by different ROIs, the degree of enhancement measured by ROI (2) and the enhanced heterogeneity measured by ROI (3) performed better than ROI (1) in distinguishing AML.wovf from ccRCC. The receiver operating characteristic (ROC) curves showed that the area under the curve (AUC) of RER_CMP (2), RER_NP (2) measured by ROI (2) and HDT_CMP and SHR_CMP measured by ROI (3) were higher . Prediction models that incorporated demographic data, morphological features and quantitative data derived from the enhanced phase were superior to quantitative data derived from the pre-contrast phase in differentiating between AML.wovf and ccRCC. Among them, the model in CMP was the best prediction model with the highest AUC (AUC = 0.986).The combination of quantitative data obtained by specific ROI in CMP can be used as a simple quantitative tool to distinguish AML.wovf from ccRCC, which has a high diagnostic value after combining demographic data and morphological features. Renal tumors with a diameter of 4 cm or less are usually called small renal tumors in clinical practice . With thCT is the first-line imaging method for the diagnosis of renal tumors –13. AnalRosenkrantz et al.  studied This study was a retrospective case–control study. It was approved by Ethical Committee of our Hospital, and the requirement for written informed consent was waived because of the retrospective nature of the study. The images used in this study were all anonymized.Between October 2016 and April 2020, 358 patients with suspected renal tumors underwent CT examination before surgery. The inclusion criteria of this study were as follows: (1) The maximum cross-sectional diameter of the tumor on CT image should be 4 cm or less; (2) The patients underwent four-phase  CT scan in our department within 2 weeks before the operation; (3) The CT slice thickness was 1 mm or less; (4) There was no obvious visual macroscopic fat within the renal masses on CT (PCP); (5) Surgical histopathology confirmed either AML or ccRCC in all lesions.253 patients were excluded because: (1) The maximum diameter of the tumor was more than 4 cm (n = 104); (2) Incomplete four-phase CT (n = 27) of which 12 patients with missing NP and 15 patients with missing EP; (3) The CT slice thickness was more than 1 mm (n = 68); (4) Tumors had typical macroscopic fat on CT (PCP) (n = 16); (5) Histopathological diagnosis of lesions was neither AML nor ccRCC (n = 38), including papillary RCC (n = 15), chromophobe RCC (n = 12), urothelial cancer (n = 4), metastasis (n = 1), and oncocytoma (n = 6).As a result, our cohort comprised of 105 patients  with 105 small renal tumors. They were divided into ccRCC group (n = 74) and AML.wovf group (n = 31) . Automated tube current modulation based on the patient’s body weight was used, and the CT scanning parameters were follows: the tube voltage was 120 kV, the collimation width was 0.625 mm, the scanning thickness was 5 mm, and the reconstruction thickness was 1 mm. The PCP, CMP, NP, and EP of the CT examination were acquired for each patient with the following protocol: the PCP was performed first, and then a nonionic contrast agent  was injected intravenously utilizing a high-pressure syringe at a rate of 4.5 ml/s for enhanced scan. CMP, NP and EP were obtained post contrast administration by applying bolus tracking .Blinded consensus reading was performed by two radiologists with 10 and 12 years of experience in abdominal imaging. Image analysis was performed utilizing a radiology information system/picture archiving and communication systems  and by assessing axial and multi-planar reconstructed (MPR) images. The analysis content included location (left kidney/right kidney), growth pattern of tumor (endophytic/exophytic), pseudocapsule sign, cystic degeneration and angular interface. The pseudocapsule sign, cystic degeneration and angular interface were determined in NP or EP. The pseudocapsule sign on CT imaging was defined as the low attenuation rim around the mass which likely correlates to histopathologically proven fibrous tissue surrounding the renal mass. . The ang2 for ROI (1) and 10 ~ 20 mm2 for ROI (2)  on the axial image with 1 mm slice thickness to determine the quantitative parameters of the tumor. Firstly, two different ROIs [ROI (1) and ROI 2)] were used to determine the attenuation value of tumor (AVT) in CMP, and the attenuation value of cortex (AVC) was measured at the adjacent renal cortex where the enhancement was homogeneous. Both ROI (1) and ROI (2) were placed on the portion of the tumor demonstrating the greatest enhancement, with an area of about 50 ~ 100 mm] were usROI 3) was placed on the largest cross-sectional image of the tumor to measure the heterogeneous degree (HD), which was recorded as standard deviation (SD) of the CT attenuation value measured by ROI (3). Since the boundary of tumor were most clearly shown in EP, the HD of tumor (HDT) was measured at this phase first, and the HD of psoas (HDP) was measured at the same level. The determination of HDT in PCP, CMP and NP should refer to the position of ROI in EP  curves were constructed and the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of statistically significant quantitative parameters. Multivariate analysis was performed on demographic data, morphological features, and quantitative parameters with significant difference by using logistic regression. The main factors were screened for differentiating AML.wovf and ccRCC in each scanning phase, and prediction models were established based on four scanning phases. The sensitivity, specificity and accuracy of the model were calculated, while the AUC and its 95% confidence interval of ROC curve were also calculated to evaluate predictive performance of the model. P < 0.05 were considered statistically significant.All data analysis was performed using IBM SPSS Statistics . Chi-square test was used to compare the qualitative data. Interclass correlation coefficient (ICC) was used to evaluate the inter-observer agreement of quantitative parameters. Firstly, Kolmogorov–Smirnov test was used to evaluate the normality of quantitative data. The independent sample P < 0.01). AML.wovf was significantly more common in females, while ccRCC was more common in males. There was no significant difference in age between the two tumors(P > 0.05) and 31 patients with AML.wovfs  in our cohort. There was significant statistical difference on gender between the two tumors (P < 0.05). There was no statistical difference in size, location, growth pattern between the two tumors (P > 0.05) (Table The psuedocapsule sign (25 out of 74 versus 2 out of 31) and cystic degeneration (36 out of 74 versus 6 out of 31) were seen more frequently in ccRCCs compared to AML.wovfs. In contrary, an angular interface was less commonly seen in ccRCCs compared to AML.wovfs (9 out of 74 versus 13 out of 31) , the ICC of AVT_CMP, AVT_NP and AVT_EP measured by ROI (2) were 0.881, 0.874 and 0.827 (P < 0.001), respectively. An ICC greater than 0.75 was considered to be of good agreement.We compared the quantitative data measured by two radiologists, and the ICC was found to be 0.808 to 0.961. Among them, the ICC of AVT_CMP, AVT_NP and AVT_EP measured by ROI (1) were 0.921, 0.886 and 0.852 (P < 0.05). There was no statistical difference between AVT_NP (1), NEV_NP (1) and quantitative parameters of EP determined by ROI (1) (P > 0.05). Among the quantitative parameters determined by ROI (2), only AVT_EP (2) had no statistical difference (P > 0.05), and the remaining quantitative parameters of ccRCC were all statistically higher than AML.wovf (P < 0.05). In terms of heterogeneous degree, HDT and SHR of ccRCC determined by ROI (3) were statistically higher than AML.wovf in each enhanced phase (P < 0.05). There was no statistically significant difference between the two tumors in HDT_PCP, SHR_PCP, and enhancement degree of each phase determined by ROI (3) (P > 0.05) (Table AVT_PCP (1), AVT_CMP (1), NEV_CMP (1), RER_CMP (1), and RER_NP (1) determined by ROI (1) showed statistical differences between ccRCC and AML.wovf , RER_NP (2), HDT_CMP and SHR_CMP were higher , and the AUC of other quantitative parameters were all lower than 0.80  were the main factors for differentiating AML.wovf and ccRCC in PCP (P < 0.05); gender, cystic degeneration, RER_CMP (2) and SHR_CMP were the main factors for differentiating two tumors in CMP (P < 0.05); gender, pseudocapsule sign, RER_NP (2) and HDT_NP were the main factors for differentiating two tumors in NP (P < 0.05); gender, angular interface, RER_EP (2) and SHR_EP were the main factors for differentiating two tumors in EP (P < 0.01)  Table .Table 4The prediction model including demographic data, morphological features and quantitative data showed that the accuracy, sensitivity and specificity of Model_PCP was 84.8%, 93.2% and 64.5% respectively. The accuracy, sensitivity and specificity of Model_CMP were 93.3%, 95.9% and 87.1% respectively. The accuracy, sensitivity and specificity of Model_NP were 89.5%, 94.6% and 77.4% respectively. The accuracy, sensitivity and specificity of Model_EP were 84.8%, 90.5% and 71.0% respectively. The AUC of these four models were 0.898, 0.986, 0.935 and 0.902, respectively  and ROI (3) were used to simulate previous studies and a smaller ROI (2) was added for comparison. The results showed that the quantitative data determined by ROI (2) with the smallest region had the best diagnostic performance whose value was generally higher than ROI (1) and ROI (3), especially in ccRCC and CMP. In CMP, the blood supply of ccRCC was unbalanced and cystic degeneration rate was high. As the range of ROI (3) included ischemic and cystic degeneration region of tumor, resulting in the lowest enhanced value and the least significant difference between ccRCC and AML.wovf determined by ROI (3). Although the region of ROI (1) was relatively small, it still inevitably included a part of area with poor blood supply or a little distance from the blood supply vessels, even a little cystic degeneration area. Therefore, its diagnostic performance was not as good as ROI (2), which only measured rich blood supply region. Due to the malignant biology of ccRCC, the basement membrane of tumor blood vessels was incomplete and highly permeable, so the contrast agent was more easily and quickly diffused into the adjacent tissue space than in AML.wovf, which making the enhancement range of this region more obvious . By measThe difficulty of placing ROI (2) is to identify the rich blood supply region. We found that on thin-section image, the rich blood supply regions of tumors tended to be randomly distributed as irregular small pieces or nodules, which were generally located at the edge of the tumors’ obvious enhancement areas, while the tumor vessels presented as thin strips or earthworm-like linear structures with clear boundaries. These two rich blood supply structures can be distinguished by careful identification. Although the placement of ROI (2) has certain requirements for diagnostic experience of radiologists, it can be better recognized by understanding the morphological characteristics of the rich blood supply region combined with 1 mm thin-section image. In our study, there was a good inter-observer agreement between the quantitative data of two radiologists, which reflected good practicability of this placement method. We also found that using ROI (2) with the smallest region in PCP did not improve ability of differentiation, which was similar to Davenport et al. .Enhanced homogeneity is another commonly used indicator to distinguish AML.wovf from ccRCC. ccRCC is mostly characterized by heterogeneous enhancement, while AML.wovf is characterized by homogeneous enhancement , 16. HowOur study indicated that HDT and SHR of ccRCC in each enhanced phase were all statistically higher than those of AML.wovf, among which the diagnostic performance of CMP was the best. In CMP, the relatively high cystic degeneration rate and local ischemic change in ccRCC lead to the decrease of pixel value in this region, which was significantly different from significantly enhanced tumor vessels. Moreover, its malignant biological characteristics made the distribution of tumor’s blood supply unbalanced, and the diffusion degree of contrast agent in each region varies greatly, so its SD value was significantly higher than AML.wovf. In NP and EP, the HDT and SHR of ccRCC were also higher than those of AML.wovf, but the difference was lower than that in CMP, which may be related to gradual increase of pixel value and decrease of SD value caused by slow infiltration of contrast agent in partial ischemic region of ccRCC. However, due to the continuous non-enhancing of complete necrosis and cystic degeneration region, the enhanced homogeneity of ccRCC was still different from AML.wovf which was mainly enhanced by smooth muscle. We applied HDP to standardize HDT in our study to reduce the effect of Image noise which could affect the value of SD. Leng et al.  showed tIn recent years, texture analysis has been used to quantify the heterogeneity of tumor and to distinguish AML.wovf from ccRCC with the AUC ranging from 0.68 to 0.988, which showed a good classification efficiency and potential , 31–34. This study shows that demographic data and morphological features are also valuable in differential diagnosis, especially gender was identified as the main factor in all prediction models. AML.wovf is significantly more common in females , cystic Our study had several limitations. First, this study is a retrospective study of a small sample from a single center, and the selection of samples may be biased. The placement of ROI in most enhancing area is also subjective as it will depend on the observer's assessment and can be variable. Therefore, the results of our study need to be verified by a large sample from multi-centers. Second, the histopathological basis reflected by quantitative data in this study needs to be further verified by comparative study between imaging and pathology. Third, the difference of scanning phase and contrast agent will affect the value of enhanced quantitative parameters, especially HDT. Although we have applied a relative ratio with HDP to weaken this effect, its impact on final diagnostic performance needs further study. Fourth, we have not performed texture analysis on these samples and further compared with the results of our study, the differences and correlations between these two quantitative analysis methods need to be explored.In conclusion, the specific ROI combination that reflects the differences of enhancement degree and heterogeneous degree can be used as a simply convenient quantitative tool to differentiate AML.wovf from ccRCC, among which single CMP has the highest diagnostic performance and great application potential.