An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study
- PMID: 33328045
- DOI: 10.1016/S2589-7500(20)30159-X
An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study
Abstract
Background: There is high demand to develop computer-assisted diagnostic tools to evaluate prostate core needle biopsies (CNBs), but little clinical validation and a lack of clinical deployment of such tools. We report here on a blinded clinical validation study and deployment of an artificial intelligence (AI)-based algorithm in a pathology laboratory for routine clinical use to aid prostate diagnosis.
Methods: An AI-based algorithm was developed using haematoxylin and eosin (H&E)-stained slides of prostate CNBs digitised with a Philips scanner, which were divided into training (1 357 480 image patches from 549 H&E-stained slides) and internal test (2501 H&E-stained slides) datasets. The algorithm provided slide-level scores for probability of cancer, Gleason score 7-10 (vs Gleason score 6 or atypical small acinar proliferation [ASAP]), Gleason pattern 5, and perineural invasion and calculation of cancer percentage present in CNB material. The algorithm was subsequently validated on an external dataset of 100 consecutive cases (1627 H&E-stained slides) digitised on an Aperio AT2 scanner. In addition, the AI tool was implemented in a pathology laboratory within routine clinical workflow as a second read system to review all prostate CNBs. Algorithm performance was assessed with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity, as well as Pearson's correlation coefficient (Pearson's r) for cancer percentage.
Findings: The algorithm achieved an AUC of 0·997 (95% CI 0·995 to 0·998) for cancer detection in the internal test set and 0·991 (0·979 to 1·00) in the external validation set. The AUC for distinguishing between a low-grade (Gleason score 6 or ASAP) and high-grade (Gleason score 7-10) cancer diagnosis was 0·941 (0·905 to 0·977) and the AUC for detecting Gleason pattern 5 was 0·971 (0·943 to 0·998) in the external validation set. Cancer percentage calculated by pathologists and the algorithm showed good agreement (r=0·882, 95% CI 0·834 to 0·915; p<0·0001) with a mean bias of -4·14% (-6·36 to -1·91). The algorithm achieved an AUC of 0·957 (0·930 to 0·985) for perineural invasion. In routine practice, the algorithm was used to assess 11 429 H&E-stained slides pertaining to 941 cases leading to 90 Gleason score 7-10 alerts and 560 cancer alerts. 51 (9%) cancer alerts led to additional cuts or stains being ordered, two (4%) of which led to a third opinion request. We report on the first case of missed cancer that was detected by the algorithm.
Interpretation: This study reports the successful development, external clinical validation, and deployment in clinical practice of an AI-based algorithm to accurately detect, grade, and evaluate clinically relevant findings in digitised slides of prostate CNBs.
Funding: Ibex Medical Analytics.
Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.
Comment in
-
Clinical deployment of AI for prostate cancer diagnosis.Lancet Digit Health. 2020 Aug;2(8):e383-e384. doi: 10.1016/S2589-7500(20)30163-1. Lancet Digit Health. 2020. PMID: 33328042 No abstract available.
Similar articles
-
Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.Lancet Oncol. 2020 Feb;21(2):222-232. doi: 10.1016/S1470-2045(19)30738-7. Epub 2020 Jan 8. Lancet Oncol. 2020. PMID: 31926806
-
Development and Validation of an Artificial Intelligence-Powered Platform for Prostate Cancer Grading and Quantification.JAMA Netw Open. 2021 Nov 1;4(11):e2132554. doi: 10.1001/jamanetworkopen.2021.32554. JAMA Netw Open. 2021. PMID: 34730818 Free PMC article.
-
Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens.JAMA Oncol. 2020 Sep 1;6(9):1372-1380. doi: 10.1001/jamaoncol.2020.2485. JAMA Oncol. 2020. PMID: 32701148 Free PMC article.
-
Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer in Biopsies-Current Status and Next Steps.Eur Urol Focus. 2021 Jul;7(4):687-691. doi: 10.1016/j.euf.2021.07.002. Epub 2021 Aug 12. Eur Urol Focus. 2021. PMID: 34393083 Review.
-
A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading.Prostate Cancer Prostatic Dis. 2023 Dec;26(4):681-692. doi: 10.1038/s41391-023-00673-3. Epub 2023 Apr 25. Prostate Cancer Prostatic Dis. 2023. PMID: 37185992 Review.
Cited by
-
Ex Vivo Fluorescence Confocal Microscopy (FCM) Ensures Representative Tissue in Prostate Cancer Biobanking: A Feasibility Study.Int J Mol Sci. 2022 Oct 11;23(20):12103. doi: 10.3390/ijms232012103. Int J Mol Sci. 2022. PMID: 36292970 Free PMC article.
-
Data-driven research on eczema: Systematic characterization of the field and recommendations for the future.Clin Transl Allergy. 2022 Jun 7;12(6):e12170. doi: 10.1002/clt2.12170. eCollection 2022 Jun. Clin Transl Allergy. 2022. PMID: 35686200 Free PMC article.
-
Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel). 2023 Aug 14;13(16):2676. doi: 10.3390/diagnostics13162676. Diagnostics (Basel). 2023. PMID: 37627935 Free PMC article. Review.
-
Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?Cancers (Basel). 2023 Jul 26;15(15):3796. doi: 10.3390/cancers15153796. Cancers (Basel). 2023. PMID: 37568612 Free PMC article. Review.
-
Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.Cancers (Basel). 2021 Nov 3;13(21):5522. doi: 10.3390/cancers13215522. Cancers (Basel). 2021. PMID: 34771684 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical