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Cited by 57AI in cancer detectionRadiomics and Machine Learning in Medical ImagingColorectal Cancer Surgical Treatments

Pathologist validation of a machine learning–derived feature for colon cancer risk stratification

Open access
L’Imperio, Vincenzo, Wulczyn, Ellery, Plass, Markus, Müller, Heimo, Tamini, Nicolò, Gianotti, Luca, Zucchini, Nicola, Reihs, Robert +9 more·2023·JAMA Network Open
DOI 10.1001/jamanetworkopen.2022.54891PMC PMC10015309
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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 categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80). Conclusions and Relevance: In this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.

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  • Several validation studies of histopathology-based prognostic models for colorectal cancer exist, including one showing a prognostic feature previously identified through AI can be learned and used by pathologists.

    “There have additionally been several validation studies of histopathology-based prognostic models (95,96), including a study demonstrating that a prognostic feature previously identified through AI analysis can be learned and used by pathologists (95).”

    From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [95]

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Authors
L’Imperio, V., Wulczyn, E., Plass, M., Müller, H., Tamini, N., Gianotti, L., Zucchini, N., Reihs, R., Corrado, G. S., Webster, D. R., Peng, L. H., Chen, P.-H. C., Lavitrano, M., Liu, Y., Steiner, D. F., Zatloukal, K., & Pagni, F.
Affiliations
  • University of Milano-Bicocca (IT)
  • Google (United States) (US)
  • Medical University of Graz (AT)
  • Azienda Ospedaliera San Gerardo (IT)
Corresponding author
  • Steiner, David F.
  • Pagni, Fabio
ORCID
Venue
JAMA Network Open
Publisher
American Medical Association (AMA)
Published
2023-03-14
Volume
6
Issue
3
Pages
e2254891
ISSN
2574-3805 (ISSN-L)
DOI
10.1001/jamanetworkopen.2022.54891
PMC
PMC10015309
OpenAlex
W4324129113
Cited by
57
References
19
Citations by year
2026: 9 · 2025: 20 · 2024: 22 · 2023: 6
FWCI
5.36
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • JAMA Network Open (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • BOA (University of Milano-Bicocca) (submittedVersion, cc-by-nc-nd)
Funders
Google
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
Keywords
colon cancer, colon adenocarcinoma, prognostic factors, risk stratification, overall survival, disease-specific survival, interobserver agreement, deep learning, histopathological features, tumor microenvironment
MeSH terms
Machine Learning, Pathologists, Adenocarcinoma, Aged, Artificial Intelligence, Colonic Neoplasms, Humans, Male, Risk Assessment

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