Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy.

Autor: Oliveira LD; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Lu J; Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Erak E; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Mendes AA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Dairo O; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Ertunc O; Suleyman Demirel University School of Medicine, Isparta, Turkey., Kulac I; Koç University School of Medicine, Istanbul, Turkey., Baena-Del Valle JA; Fundacion Santa Fe de Bogota University Hospital, Bogota, Colombia., Jones T; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Hicks JL; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Glavaris S; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Guner G; Faculty of Medicine, Hacettepe University, Ankara, Turkey., Vidal ID; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Trock BJ; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Joshi U; AIRA Matrix Private Limited, Mumbai, India., Kondragunta C; AIRA Matrix Private Limited, Mumbai, India., Bonthu S; AIRA Matrix Private Limited, Mumbai, India., Joshu C; Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Singhal N; AIRA Matrix Private Limited, Mumbai, India., De Marzo AM; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Lotan TL; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: tlotan1@jhmi.edu.
Jazyk: angličtina
Zdroj: European urology oncology [Eur Urol Oncol] 2024 Sep 03. Date of Electronic Publication: 2024 Sep 03.
DOI: 10.1016/j.euo.2024.08.004
Abstrakt: Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; however, interobserver variability remains a challenge. Artificial intelligence algorithms applied to histopathologic images standardize grading, but most have been tested only for agreement with pathologist GG, without assessment of performance with respect to oncologic outcomes. We compared deep learning-based and pathologist-based GGs for an association with metastatic outcome in three surgical cohorts comprising 777 unique patients. A digitized whole slide image of the representative hematoxylin and eosin-stained slide of the dominant tumor nodule was assigned a GG by an artificial intelligence-based grading algorithm and was compared with the GG assigned by a contemporary pathologist or the original pathologist-assigned GG for the entire prostatectomy. Harrell's C-indices based on Cox models for time to metastasis were compared. In a combined analysis of all cohorts, the C-index for the artificial intelligence-assigned GG was 0.77 (95% confidence interval [CI]: 0.73-0.81), compared with 0.77 (95% CI: 0.73-0.81) for the pathologist-assigned GG. By comparison, the original pathologist-assigned GG for the entire case had a C-index of 0.78 (95% CI: 0.73-0.82). PATIENT SUMMARY: Artificial intelligence-enabled prostate cancer grading on a single slide was comparable with pathologist grading for predicting metastatic outcome in men treated by radical prostatectomy, enabling equal access to expert grading in lower resource settings.
(Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: