Evidence map›Paper›PMID 42890536›Full record

ArticleFrontiers in oncology2026

New insights into tumor size in colorectal cancer: smaller size, higher risk?

Ruipeng Ding, Tianyu Li, Cong Li, Wang Song, Xianhua Gao, Midie Xu, Sheng Wang

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Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Ruipeng DingDepartment of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Tianyu LiDepartment of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Cong LiDepartment of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Wang SongDepartment of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Xianhua GaoDepartment of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China.
Midie XuDepartment of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Sheng WangDepartment of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In colorectal cancer, the AJCC T category is defined by the depth of bowel wall invasion rather than by the absolute size of the primary tumor. The clinical value of tumor size in assessing tumor aggressiveness, metastatic risk, and prognosis remains insufficiently investigated. Aim: To evaluate the clinical value of tumor size in assessing potential metastatic risk and prognostic stratification in colorectal cancer. Methods: We retrospectively included 10,513 patients with colorectal cancer who underwent surgical treatment at FUSCC between 2008 and 2020. We integrated tumor size with clinicopathological characteristics to assess metastatic risk using machine-learning models and SHAP-based interpretation. By modeling tumor size as both a categorical and continuous variable, we further investigated its prognostic relevance using Kaplan-Meier survival analysis and Cox proportional hazards regression incorporating restricted cubic splines. Additionally, an independent cohort of 6,739 patients from Changhai Hospital was used for external validation. Results: Machine-learning identified lymph node metastasis as the primary predictive endpoint, with the CatBoost model achieving the highest AUROC of 0.800. SHAP analysis showed that smaller tumor diameter contributed positively to predicted metastatic risk. Multivariable restricted cubic spline analysis further demonstrated a significant nonlinear association between tumor diameter and OS, with mortality risk increasing as tumor diameter decreased within the approximately 0-3 cm range. Among younger male patients with locally advanced disease, tumors ≤3 cm were associated with significantly worse OS and DFS than tumors >5 cm (both Conclusion: Smaller tumor size may paradoxically indicate higher lymph node metastatic risk and poorer prognosis in a subset of patients with colorectal cancer, particularly younger male patients with locally advanced disease. Tumor size may therefore serve as a complementary factor for postoperative risk stratification beyond conventional TNM staging.

Indexed as

colorectal cancermachine learningmetastasisrisk factorstumor size

Identifiers

PMID42890536
PMCPMC13671944

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