Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer.
OBJECTIVE
To develop a new digital biomarker based on the analysis of primary tumour tissue by a convolutional neural network (CNN) to predict lymph node metastasis (LNM) in a cohort matched for already established risk factors.


PATIENTS AND METHODS
Haematoxylin and eosin (H&E) stained primary tumour slides from 218 patients (102 N+; 116 N0), matched for Gleason score, tumour size, venous invasion, perineural invasion and age, who underwent radical prostatectomy were selected to train a CNN and evaluate its ability to predict LN status.


RESULTS
With 10 models trained with the same data, a mean area under the receiver operating characteristic curve (AUROC) of 0.68 (95% confidence interval [CI] 0.678-0.682) and a mean balanced accuracy of 61.37% (95% CI 60.05-62.69%) was achieved. The mean sensitivity and specificity was 53.09% (95% CI 49.77-56.41%) and 69.65% (95% CI 68.21-71.1%), respectively. These results were confirmed via cross-validation. The probability score for LNM prediction was significantly higher on image sections from N+ samples (mean [SD] N+ probability score 0.58 [0.17] vs 0.47 [0.15] N0 probability score, P = 0.002). In multivariable analysis, the probability score of the CNN (odds ratio [OR] 1.04 per percentage probability, 95% CI 1.02-1.08; P = 0.04) and lymphovascular invasion (OR 11.73, 95% CI 3.96-35.7; P < 0.001) proved to be independent predictors for LNM.


CONCLUSION
In our present study, CNN-based image analyses showed promising results as a potential novel low-cost method to extract relevant prognostic information directly from H&E histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.
['Wessels|Frederik|F|0000-0003-4213-8692', 'Schmitt|Max|M|', 'Krieghoff-Henning|Eva|E|', 'Jutzi|Tanja|T|', 'Worst|Thomas S|TS|', 'Waldbillig|Frank|F|', 'Neuberger|Manuel|M|', 'Maron|Roman C|RC|', 'Steeg|Matthias|M|', 'Gaiser|Timo|T|', 'Hekler|Achim|A|', 'Utikal|Jochen S|JS|', 'von Kalle|Christof|C|', 'Fröhling|Stefan|S|', 'Michel|Maurice S|MS|', 'Nuhn|Philipp|P|', 'Brinker|Titus J|TJ|0000-0002-3620-5919']
D000368:Aged D000077321:Deep Learning D006801:Humans D008207:Lymphatic Metastasis D008297:Male D008875:Middle Aged D060787:Neoplasm Grading D016571:Neural Networks, Computer D011379:Prognosis D011471:Prostatic Neoplasms D012189:Retrospective Studies
deep learning convolutional neural network #PCSM #ProstateCancer #uroonc artificial intelligence machine learning neoplasm metastasis prostatic neoplasms