1. Deep learning approach to predict lymph node metastasis directly from primary tumour histology in prostate cancer
- Author
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Tanja B. Jutzi, Frederik Wessels, Christof von Kalle, Stefan Fröhling, Thomas Stefan Worst, Achim Hekler, Jochen Utikal, Matthias Steeg, Max Schmitt, Roman C. Maron, Titus J. Brinker, Philipp Nuhn, Maurice Stephan Michel, Eva Krieghoff-Henning, Timo Gaiser, Frank Waldbillig, and Manuel Neuberger
- Subjects
Male ,0301 basic medicine ,medicine.medical_specialty ,Lymphovascular invasion ,Urology ,medicine.medical_treatment ,Perineural invasion ,03 medical and health sciences ,Prostate cancer ,Deep Learning ,0302 clinical medicine ,Humans ,Medicine ,Aged ,Retrospective Studies ,Receiver operating characteristic ,business.industry ,Prostatectomy ,Prostatic Neoplasms ,Histology ,Odds ratio ,Middle Aged ,Prognosis ,medicine.disease ,Confidence interval ,030104 developmental biology ,Lymphatic Metastasis ,030220 oncology & carcinogenesis ,Neural Networks, Computer ,Neoplasm Grading ,business - Abstract
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.Haematoxylin and eosin (HE) 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.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; P0.001) proved to be independent predictors for LNM.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 HE histology to predict the LN status of patients with prostate cancer. Our ubiquitously available technique might contribute to an improved LN status prediction.
- Published
- 2021
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