1. Pathologist Validation of a Machine Learning-Derived Feature for Colon Cancer Risk Stratification
- Author
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L'Imperio, V, Wulczyn, E, Plass, M, Müller, H, Tamini, N, Gianotti, L, Zucchini, N, Reihs, R, Corrado, G, Webster, D, Peng, L, Chen, P, Lavitrano, M, Liu, Y, Steiner, D, Zatloukal, K, Pagni, F, L'Imperio, Vincenzo, Wulczyn, Ellery, Plass, Markus, Müller, Heimo, Tamini, Nicolò, Gianotti, Luca, Zucchini, Nicola, Reihs, Robert, Corrado, Greg S, Webster, Dale R, Peng, Lily H, Chen, Po-Hsuan Cameron, Lavitrano, Marialuisa, Liu, Yun, Steiner, David F, Zatloukal, Kurt, Pagni, Fabio, L'Imperio, V, Wulczyn, E, Plass, M, Müller, H, Tamini, N, Gianotti, L, Zucchini, N, Reihs, R, Corrado, G, Webster, D, Peng, L, Chen, P, Lavitrano, M, Liu, Y, Steiner, D, Zatloukal, K, Pagni, F, L'Imperio, Vincenzo, Wulczyn, Ellery, Plass, Markus, Müller, Heimo, Tamini, Nicolò, Gianotti, Luca, Zucchini, Nicola, Reihs, Robert, Corrado, Greg S, Webster, Dale R, Peng, Lily H, Chen, Po-Hsuan Cameron, Lavitrano, Marialuisa, Liu, Yun, Steiner, David F, Zatloukal, Kurt, and Pagni, Fabio
- Abstract
Importance: Identifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning-derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists. Objective: To evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer. Design, Setting, and Participants: This prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort. Main Outcomes and Measures: Prognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated. Results: A total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorica
- Published
- 2023