Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Wulczyn, E; Nagpal, K; Symonds, M; Moran, M; Plass, M; Reihs, R; Nader, F; Tan, F; Cai, Y; Brown, T; Flament-Auvigne, I; Amin, MB; Stumpe, MC; Müller, H; Regitnig, P; Holzinger, A; Corrado, GS; Peng, LH; Chen, PC; Steiner, DF; Zatloukal, K; Liu, Y; Mermel, CH.
Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading.
Commun Med (Lond). 2021; 1: 10
Doi: 10.1038/s43856-021-00005-3
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- Führende Autor*innen der Med Uni Graz
- Zatloukal Kurt
- Co-Autor*innen der Med Uni Graz
- Holzinger Andreas
- Müller Heimo
- Nader Farah
- Plass Markus
- Regitnig Peter
- Reihs Robert
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- Abstract:
- BACKGROUND: Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication. METHODS: In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5-25 years of follow-up (median: 13, interquartile range 9-17). RESULTS: Here, we show that the A.I.'s risk scores produced a C-index of 0.84 (95% CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78-0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.'s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71-0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01-0.15) and 0.07 (95% CI 0.00-0.14), respectively. CONCLUSIONS: Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.