Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

• URL: http://arxiv.org/abs/2106.14256v1
• Date: Sun, 27 Jun 2021 15:21:33 GMT
• Title: Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
• Authors: Boing Liu, Yinxi Wang, Philippe Weitz, Johan Lindberg, Lars Egevad, Henrik Gr\"onberg, Martin Eklund, Mattias Rantalainen
• Abstract summary: We developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images. The proposed model has the potential to reduce the number of false negative cases in routine systematic prostate biopsies.
• Score: 0.7739635712759623
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biopsies only sample a relatively small volume of the prostate, and tumour lesions in regions between biopsy cores can be missed, leading to a well-known low sensitivity to detect clinically relevant cancer. As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer. Methods: This study included 14,354 hematoxylin and eosin stained whole slide images from benign prostate biopsies from 1,508 men in two groups: men without an established prostate cancer (PCa) diagnosis and men with at least one core biopsy diagnosed with PCa. 80% of the participants were assigned as training data and used for model optimization (1,211 men), and the remaining 20% (297 men) as a held-out test set used to evaluate model performance. An ensemble of 10 deep convolutional neural network models was optimized for classification of biopsies from men with and without established cancer. Hyperparameter optimization and model selection was performed by cross-validation in the training data . Results: Area under the receiver operating characteristic curve (ROC-AUC) was estimated as 0.727 (bootstrap 95% CI: 0.708-0.745) on biopsy level and 0.738 (bootstrap 95% CI: 0.682 - 0.796) on man level. At a specificity of 0.9 the model had an estimated sensitivity of 0.348. Conclusion: The developed model has the ability to detect men with risk of missed PCa due to under-sampling of the prostate. The proposed model has the potential to reduce the number of false negative cases in routine systematic prostate biopsies and to indicate men who could benefit from MRI-guided re-biopsy.

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