Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

By Wouter Bulten, H. Pinckaers, H. V. Van Boven, R. Vink, Thomas de Bel, B. van Ginneken, J. A. van der Laak, C. Hulsbergen–van de Kaa, Geert J. S. Litjens. The Lancet Oncology, 2019

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BACKGROUND The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater concordance; however, such expertise is not widely available. Prostate cancer diagnostics could thus benefit from robust, reproducible Gleason grading. We aimed to investigate the potential of deep learning to perform automated Gleason grading of prostate biopsies. METHODS In this retrospective study, we developed a deep-learning system to grade prostate biopsies following the Gleason grading standard. The system was developed using randomly selected biopsies, sampled by the biopsy Gleason score, from patients at the Radboud University Medical Center (pathology report dated between Jan 1, 2012, and Dec 31, 2017). A semi-automatic labelling technique was used to circumvent the need for manual annotations by pathologists, using pathologists' reports as the reference standard during training. The system was developed to delineate individual glands, assign Gleason growth patterns, and determine the biopsy-level grade. For validation of the method, a consensus reference standard was set by three expert urological pathologists on an independent test set of 550 biopsies. Of these 550, 100 were used in an observer experiment, in which the system, 13 pathologists, and two pathologists in training were compared with respect to the reference standard. The system was also compared to an external test dataset of 886 cores, which contained 245 cores from a different centre that were independently graded by two pathologists. FINDINGS We collected 5759 biopsies from 1243 patients. The developed system achieved a high agreement with the reference standard (quadratic Cohen's kappa 0·918, 95% CI 0·891-0·941) and scored highly at clinical decision thresholds: benign versus malignant (area under the curve 0·990, 95% CI 0·982-0·996), grade group of 2 or more (0·978, 0·966-0·988), and grade group of 3 or more (0·974, 0·962-0·984). In an observer experiment, the deep-learning system scored higher (kappa 0·854) than the panel (median kappa 0·819), outperforming 10 of 15 pathologist observers. On the external test dataset, the system obtained a high agreement with the reference standard set independently by two pathologists (quadratic Cohen's kappa 0·723 and 0·707) and within inter-observer variability (kappa 0·71). INTERPRETATION Our automated deep-learning system achieved a performance similar to pathologists for Gleason grading and could potentially contribute to prostate cancer diagnosis. The system could potentially assist pathologists by screening biopsies, providing second opinions on grade group, and presenting quantitative measurements of volume percentages. FUNDING Dutch Cancer Society.

Subfields: Artificial Intelligence, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/24b8cee45431f633d2fa6e3c05670f62b1e41e7e

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Citations in the corpus

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, result): “Bulten et al. [210] and Strom et al. [211] both recently published large cohort studies involving 1243 and 976 patients respectively, that DL approaches could be used for achieving a performance of Gleason grading that was comparable to pathologists [212].”
Deep learning in histopathology: the path to the clinic (2021, methodology): “Highlighting regions of prostate cancer using different colors to represent different Gleason grades 4,66 and highlighting lung cancer growth patterns by adenocarcinoma subtype 38,67 using CPATH methods have produced similar results.”
Data-efficient and weakly supervised computational pathology on whole-slide images (2020, background): “urn [p Y];[y Y] layers of the network. In all our experiments, and ˝are both set to 1:0. l(s;y) = max ˆ max j2Ynfyg fs j+g s y;0 ˙ (4) L 1;˝(s;y) = ˝log " X j2Y exp 1 ˝ (1 (j6=y)+s j s y) # (5) Training Details. During training, the total loss L totalis the sum of both the slide-level classiﬁc”
Artificial intelligence for digital and computational pathology (2023, unknown): “Patches, nuclei, pixels, etc.”
Deep neural network models for computational histopathology: A survey (2019, background): “Segmentation of histological primitives such as cells, glands, nuclei and other tissue components is an essential pre-requisite for obtaining reliable morphological measurements to assess the malignancy of several carcinomas (Chen et al., 2017a; Sirinukunwattana et al., 2017; Bulten et al., 2020).”
Artificial intelligence in histopathology: enhancing cancer research and clinical oncology (2022, background): “Detection and grading of prostate cancer has received broad attention 27,33,34 due to the high prevalence of this disease and the typically large interobserver variation in histopathology assessment.”