A Deep Learning Model for Prostate Adenocarcinoma Classification in Needle Biopsy Whole-Slide Images Using Transfer Learning

Abstract

The histopathological diagnosis of prostate adenocarcinoma in needle biopsy specimens is of pivotal importance for determining optimum prostate cancer treatment. Since diagnosing a large number of cases containing 12 core biopsy specimens by pathologists using a microscope is time-consuming manual system and limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately screen large numbers of histopathological prostate needle biopsy specimens. Computational pathology applications that can assist pathologists in detecting and classifying prostate adenocarcinoma from whole-slide images (WSIs) would be of great benefit for routine pathological practice. In this paper, we trained deep learning models capable of classifying needle biopsy WSIs into adenocarcinoma and benign (non-neoplastic) lesions. We evaluated the models on needle biopsy, transurethral resection of the prostate (TUR-P), and The Cancer Genome Atlas (TCGA) public dataset test sets, achieving an ROC-AUC up to 0.978 in needle biopsy test sets and up to 0.9873 in TCGA test sets for adenocarcinoma.

Keywords: adenocarcinoma; biopsy; deep learning; prostate; transfer learning; whole-slide image.

Figure 1

(a) shows a zoomed-in example of a tile in a WSI. (b) During training, we iteratively alternated between inference and training steps. The model weights were frozen during the inference step, and this was applied in a sliding window fashion on the entire tissue regions of each WSI. The top k tiles with the highest probabilities were then selected from each WSI and placed into a queue. During training, the selected tiles from multiple WSIs formed a training batch and were used to train the model.

Figure 2

ROC curves on the biopsy (Hospitals A, B, C, and A–C), TUR-P (Hospitals A, B, and A and B), and TCGA test sets of the TL-colon poorly ADC-2 (20×, 512) model.

Figure 3

Representative true positive prostate adenocarcinoma from the biopsy test sets. On the prostate needle biopsy whole-slide image (A), Specimens #1–#4 are benign (non-neoplastic), and there are adenocarcinoma cell infiltration foci (C,E,G) in Specimens #5 and #6 based on the pathological diagnostic report, which the pathologists marked as red ink dots (yellow triangles) on the glass slides. The heat map image (B) shows the true positive prediction of adenocarcinoma cells (D,F,H) using transfer learning from the colon poorly differentiated adenocarcinoma model (TL-colon poorly ADC-2 (20×, 512)), which corresponds respectively to the H&E histopathology (C,E,G). The heat map uses the jet color map where blue indicates low probability and red indicates high probability.

Figure 4

Representative examples of prostate adenocarcinoma false positive prediction outputs on cases from the needle biopsy test sets. Histopathologically, (A,E) are benign (non-neoplastic) lesions. The heat map images (B,F) exhibit false positive predictions of adenocarcinoma (D,H) using transfer learning from the colon poorly differentiated adenocarcinoma model (TL-colon poorly ADC-2 (20×, 512)). Infiltration of chronic inflammatory cells including histiocytes, lymphocytes, and plasma cells (C) would be the primary cause of the false positives due to a morphology analogous to adenocarcinoma cells’ infiltration (D). Areas where prostatic hyperplasia (G) would be the primary cause of false positives (H). The heat map uses the jet color map where blue indicates low probability and red indicates high probability.

Figure 5

Representative false negative prostate adenocarcinoma from the needle biopsy test sets. According to the histopathological report, there were four needle biopsy specimens in the WSI, and three of them had adenocarcinomas (A). The pathologists marked the adenocarcinoma areas in blue dots (A). High-power view showing that there were adenocarcinoma foci (C–E). The heat map image (B) shows no true positive predictions of adenocarcinoma using transfer learning from the colon poorly differentiated adenocarcinoma model (TL-colon poorly ADC-2 (20×, 512)).

Figure 6

Representative true positive prostate adenocarcinoma from the transurethral resection of the prostate (TUR-P) test sets. In the TUR-P specimen (A), there are adenocarcinoma cell infiltration foci (C) based on the histopathological report. The heat map image (B) shows the true positive prediction of adenocarcinoma cells (D) using transfer learning from the colon poorly differentiated adenocarcinoma model (TL-colon poorly ADC-2 (20×, 512)). The heat map uses the jet color map where blue indicates low probability and red indicates high probability.

Figure 7

Representative examples of prostate adenocarcinoma false positive prediction outputs on cases from the transurethral resection of the prostate (TUR-P) test sets. Histopathologically, (A,E) are benign (non-neoplastic) lesions. The heat map images (B,F) exhibit false positive predictions of adenocarcinoma (D,H) using transfer learning from the colon poorly differentiated adenocarcinoma model (TL-colon poorly ADC-2 (20×, 512)). Inflammation with infiltration of inflammatory cells including foam cells (C) would be the primary cause of the false positives due to a morphology analogous to adenocarcinoma cells’ infiltration (D). The cauterized area of the marginal zone of the specimen (G) would be the primary cause of the false positives (H). The heat map uses the jet color map where blue indicates low probability and red indicates high probability.