acePreoperative Prediction of Lymph Node Metastasis in Colorectal Cancer Using Routinely Collected Clinical Data: Multicenter Retrospective Machine Learning Model Development and External Validation Study
Background Preoperative nodal assessment in colorectal cancer (CRC) remains imperfect, and uncertainty regarding pathological lymph node metastasis (LNM) may persist despite conventional imaging. Machine learning using routinely available preoperative information may provide complementary patient-level risk estimates without requiring radiomic feature extraction or molecular testing. Objective This study aimed to develop and externally validate machine learning models for preoperative estimation of pathologicall…
A random forest using six routine preoperative variables estimated lymph node metastasis risk in colorectal cancer and added predictive information beyond clinical tumor stage alone.
External discrimination was only moderate, and the model is research-stage and cannot replace imaging, pathology, or clinical judgment before prospective validation.
The model is intended only for research-stage adjunctive risk stratification, showed moderate external discrimination, and needs prospective validation in broader populations and clinical settings before implementation.
Evidence
- Peer-reviewedJMIR Cancer2026-10-07
How should this claim be treated?
Truvace Impact Record TRV-2026-1766, v1: “Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer Using Routinely Collected Clinical Data: Multicenter Retrospective Machine Learning Model Development and External Validation Study.” Truvace, 2026-10-18. /record/TRV-2026-1766 (accessed at citation time). sha256 487ade3367157275…
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