Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches.
PURPOSE
We aimed to investigate the feasibility of predicting invasion carcinoma from ductal carcinoma in situ (DCIS) lesions diagnosed by preoperative core needle biopsy using radiomics signatures, clinical imaging characteristics, and breast imaging reporting and data system (BI-RADS) descriptors on mammography.


METHODS
Retrospectively, we enrolled 362 DCIS patients diagnosed by core needle biopsy, 110 (30.4%) of which had invasive carcinoma confirmed by operation and pathology. We analyzed the images identified suspicious calcification from 250 subjects (161 pure DCIS and 89 DCIS with invasion). A total of 569 calcification radiomics signatures were extracted from microcalcification for each patient. We included a group of routine clinical imaging characteristics and BI-RADS descriptors for comparison purpose. Five feature selection and seven classification methods were evaluated in terms of their prediction performance. We compared the area under the receiver operating characteristic curve (AUC) averaged from tenfold cross-validation of different feature sets to identify the best combination of feature selection and classification methods.


RESULTS
Optimal feature selection and classification methods were identified after evaluating various combinations of feature sets. The best performance was achieved using both radiomics and clinical imaging characteristics (AUC = 0.72) performing better than BI-RADS descriptors or radiomics, but was no significant difference with clinical imaging characteristics (AUC = 0.66). The most significant features found were morphology signatures, first-order statistics, asymmetry/mass prevalence, and nuclear grade.


CONCLUSIONS
We found that the prediction model established using microcalcifications radiomics signatures and clinical imaging characteristics has the potential to identify an understaging of invasive breast cancer.
['Li|Jiao|J|', 'Song|Yan|Y|', 'Xu|Shuoyu|S|', 'Wang|Jinhua|J|', 'Huang|Huabin|H|', 'Ma|Weimei|W|', 'Jiang|Xinhua|X|', 'Wu|Yaopan|Y|', 'Cai|Hongming|H|', 'Li|Li|L|http://orcid.org/0000-0002-4712-2692']
D000328:Adult D000368:Aged D062005:Biopsy, Large-Core Needle D001943:Breast Neoplasms D002285:Carcinoma, Intraductal, Noninfiltrating D003937:Diagnosis, Differential D005260:Female D006801:Humans D008327:Mammography D008875:Middle Aged D009361:Neoplasm Invasiveness D012372:ROC Curve D012189:Retrospective Studies
Ductal carcinoma in situ Radiomics Mammography Microcalcification Machine learning