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Ruling Out Early Distant Recurrence in Stage III Colon Cancer: A Simple 4-Variable Machine Learning Model with
Shih-Feng Huang1, Chao-Wen Hsu1,2, Yu-Hsun Chen1
1Division of colorectal surgery, Kaohsiung Veterans General Hospital, Kaohsiung City, Taiwan (R.O.C.).
Background:
Early distant recurrence within 18 months reflects aggressive tumor biology in Stage III colon cancer. Current anatomical staging often fails to capture this biological heterogeneity.
Objective:
To develop and externally validate a simple, pathology-based model for ruling out early distant recurrence within 18 months in Stage III colon cancer.
Design:
Retrospective model development with external validation, following current machine-learning prediction-model reporting guidelines.
Settings:
Derivation at a tertiary referral center; validation at an independent tertiary institution.
Patients:
Three hundred thirty-one patients in the derivation cohort (62 events) and 142 in the external cohort (19 events) who underwent curative-intent colectomy for Stage III colon adenocarcinoma.
Main Outcome Measures:
Early distant recurrence within 18 months. Secondary outcomes included disease-free survival and calibration performance.
Results:
A 4-variable XGBoost model (American Joint Committee on Cancer substage, lymph node ratio, perineural invasion, and differentiation) achieved a pooled out-of-fold area under the curve of 0.680 in the derivation cohort, comparable to conventional staging (0.625). Sensitivity (77.4%) and negative predictive value (89.8%) exceeded those of conventional staging. On external validation, the model yielded an area under the curve of 0.633 (vs 0.610 for conventional staging) and a negative predictive value of 90.8%, maintaining rule out performance of approximately 90% across both cohorts despite their differing recurrence rates. High-risk patients in the external cohort had worse disease-free survival (hazard ratio 1.83; 95% confidence interval 1.06-3.16). Exploratory subgroup analyses showed directionally consistent but statistically inconclusive results.
Limitations:
Retrospective design, limited molecular data, and small numbers of events in the external cohort.
Conclusions:
A simple 4-predictor model using routine pathological variables provides consistent rule out performance for early distant recurrence within 18months across 2 independent cohorts. The model offers a practical risk-assessment tool for early postoperative counseling where molecular testing is unavailable. A web-based calculator was developed to support early postoperative risk assessment. See Video Abstract.
