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This research aims to enhance risk prediction for recurrence in microsatellite-stable colon cancer post-chemotherapy using AI-derived tumor microenvironment features.

May 29, 2026Journal of Clinical Oncology0 citations

AI-derived tumor microenvironment features and recurrence risk in microsatellite-stable colon cancer after adjuvant chemotherapy.

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CPChanghee ParkSeoul National University TPTaekeun ParkSeoul National University Hospital YLYoojoo LimUniversity of Seoul

Key Points

This research aims to enhance risk prediction for recurrence in microsatellite-stable colon cancer post-chemotherapy using AI-derived tumor microenvironment features.
Retrospective analysis of tumor microenvironment in stage II and III colon cancer patients after adjuvant chemotherapy.
Quantification of tumor and stromal areas, and TME cell types using AI-based Lunit SCOPE IO.
Development of a generalized linear model integrating AI-derived features with conventional clinicopathological variables.
High stromal lymphocyte density associated with favorable disease-free survival (DFS) (HR 0.42, 95% CI 0.26 – 0.68).
The TME-integrated model stratified patients into high- and low-risk groups, showing 3-year DFS rates of 72.3% and 93.8%, respectively (adjusted HR 3.29, 95% CI 1.98 – 5.46).
Validation in an independent cohort showed the model's robustness with adjusted HR for high-risk at 3.68 (95% CI 1.60 – 8.48).

Abstract

3646 Background: Despite adjuvant chemotherapy, a substantial number of patients with stage II–III microsatellite stable (MSS) colon cancer relapse. While clinicopathologic and circulating tumor DNA (ctDNA) analysis can be used for risk stratification, there is a need for further improvement in risk prediction. Here, we utilized artificial intelligence (AI) applied to hematoxylin and eosin (H adjusted HR for high-risk 3.68, 95% CI 1.60 – 8.48). Conclusions: TME-integrated model combining AI-derived TME features with clinicopathologic factors significantly improved recurrence risk stratification in MSS colon cancer receiving adjuvant chemotherapy.

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Park et al. (2026) studied this question.

synapsesocial.com/papers/6a192f07fab5b468c4418525 https://doi.org/10.1200/jco.2026.44.16_suppl.3646

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May 29, 2026Journal of Clinical Oncology0 citations

AI-derived tumor microenvironment features and recurrence risk in microsatellite-stable colon cancer after adjuvant chemotherapy.

View Full Paper

CPChanghee ParkSeoul National University TPTaekeun ParkSeoul National University Hospital YLYoojoo LimUniversity of Seoul

Key Points

This research aims to enhance risk prediction for recurrence in microsatellite-stable colon cancer post-chemotherapy using AI-derived tumor microenvironment features.
Retrospective analysis of tumor microenvironment in stage II and III colon cancer patients after adjuvant chemotherapy.
Quantification of tumor and stromal areas, and TME cell types using AI-based Lunit SCOPE IO.
Development of a generalized linear model integrating AI-derived features with conventional clinicopathological variables.
High stromal lymphocyte density associated with favorable disease-free survival (DFS) (HR 0.42, 95% CI 0.26 – 0.68).
The TME-integrated model stratified patients into high- and low-risk groups, showing 3-year DFS rates of 72.3% and 93.8%, respectively (adjusted HR 3.29, 95% CI 1.98 – 5.46).
Validation in an independent cohort showed the model's robustness with adjusted HR for high-risk at 3.68 (95% CI 1.60 – 8.48).

Abstract

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Cite This Study

Park et al. (2026) studied this question.

synapsesocial.com/papers/6a192f07fab5b468c4418525 https://doi.org/10.1200/jco.2026.44.16_suppl.3646

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