[1907.01368] Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
Abstract: Background: An increasing volume of prostate biopsies and a world-wide
shortage of uro-pathologists puts a strain on pathology departments.
Additionally, the high intra- and inter-observer variability in grading can
result in over- and undertreatment of prostate cancer. Artificial intelligence
(AI) methods may alleviate these problems by assisting pathologists to reduce
workload and harmonize grading.
Methods: We digitized 6,682 needle biopsies from 976 participants in the
population based STHLM3 diagnostic study to train deep neural networks for
assessing prostate biopsies. The networks were evaluated by predicting the
presence, extent, and Gleason grade of malignant tissue for an independent test
set comprising 1,631 biopsies from 245 men. We additionally evaluated grading
performance on 87 biopsies individually graded by 23 experienced urological
pathologists from the International Society of Urological Pathology. We
assessed discriminatory performance by receiver operating characteristics (ROC)
and tumor extent predictions by correlating predicted millimeter cancer length
against measurements by the reporting pathologist. We quantified the
concordance between grades assigned by the AI and the expert urological
pathologists using Cohen's kappa.
Results: The performance of the AI to detect and grade cancer in prostate
needle biopsy samples was comparable to that of international experts in
prostate pathology. The AI achieved an area under the ROC curve of 0.997 for
distinguishing between benign and malignant biopsy cores, and 0.999 for
distinguishing between men with or without prostate cancer. The correlation
between millimeter cancer predicted by the AI and assigned by the reporting
pathologist was 0.96. For assigning Gleason grades, the AI achieved an average
pairwise kappa of 0.62. This was within the range of the corresponding values
for the expert pathologists (0.60 to 0.73).
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