Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.

Shuai Jiang¹, Christina Robinson², Joseph Anderson³, William Hisey², Lynn Butterly⁴, Arief Suriawinata⁵, Saeed Hassanpour⁶

¹Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.

²Department of Gastroenterology and Hepatology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; New Hampshire Colonoscopy Registry, Lebanon, New Hampshire.

³Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; White River Junction VA Medical Center, Hartford, Vermont.

⁴Department of Gastroenterology and Hepatology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; New Hampshire Colonoscopy Registry, Lebanon, New Hampshire; Department of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.

⁵Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.

⁶Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire; Department of Computer Science, Dartmouth College, Hanover, New Hampshire. Electronic address: saeed.hassanpour@dartmouth.edu.

PubMed PMC DOI PDF

Abstract

Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines rely primarily on histopathologic features, overlooking other important CRC risk factors. Variability in polyp characterization among pathologists also hinders consistent surveillance decisions. Advances in digital pathology and deep learning enable the…

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NIH Funding

Grant #10475120
Clinicopathologic and Genetic Profiling through Machine Learning and Natural Language Processing for Precision Lung Cancer Management
DARTMOUTH COLLEGE
$367,647 (FY2022)
Grant #11331979
Advancing Digital Pathology through Novel Machine Learning Methodologies
DARTMOUTH COLLEGE
$591,896 (FY2026)
Grant #10316231
Improving Colorectal Cancer Screening and Risk Assessment through Deep Learning on Medical Images and Records
DARTMOUTH COLLEGE
$356,700 (FY2022)

Other Funding

NCI NIH HHS(R01 CA243449)
United States
NCI NIH HHS(R01 CA249758)
United States
NLM NIH HHS(R01 LM012837)
United States
NLM NIH HHS(R01 LM013833)
United States

MeSH Terms

Humans0Colorectal Neoplasms*Risk Assessment0Early Detection of Cancer*Female0Male0Middle Aged0Deep Learning0Colonoscopy0Aged0Disease Progression0Colonic Polyps0ROC Curve0

Article Info

PMID: 41109668
PMCID: PMC12881281
DOI: 10.1016/j.ajpath.2025.09.016
Received: April 13, 2025
Accepted: September 24, 2025

How to Cite

Jiang S et al. Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records. Am J Pathol2026;196(2):493-504. doi:10.1016/j.ajpath.2025.09.016

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