Purpose Accurate preoperative assessment of regional lymphatic metastases (LNM) is essential for effective surgical selection of patients with colorectal cancer (CRC). This study aimed to develop a machine learning (ML) model that integrates radiomics and clinical risk factors to predict preoperative LNM in CRC patients. Methods This multicenter cohort study retrospectively collected data from 349 CRC patients between January 1, 2020, and December 31, 2023. A total of 292 patients from our hospital comprised the training dataset, while 57 patients from external hospitals formed the validation dataset. Radiomic features of the tumor region (3D (R) ) and colorectal region (3D (C) ) were extracted from venous-phase CT images. LASSO (least absolute shrinkage and selection operator) regression was applied to screen clinical and radiomic features. 4 prediction models, clinical, 3D (R), 3D (R + C), and combined, were constructed using support vector machine (SVM). The optimal model was identified through comparative analysis of the area under the curve (AUC) metric across multiple models. Results The Model₃D (R + C) demonstrated superior discriminative performance compared to Model₃D (R) alone (AUC: training, 0. 733 (95% CI: 0. 693, 0. 773) vs. 0. 696 (95% CI: 0. 655, 0. 737) ; validation, 0. 641 (95% CI: 0. 590, 0. 692) vs. 0. 563 (95% CI: 0. 507, 0. 619) ). The model combining clinical and 3D (R + C) (ModelC₃D (R + C) ) outperformed the clinical model (ModelC) and Model₃D (R + C) (AUC: training: 0. 858 (95% CI: 0. 826, 0. 890) vs. 0. 635 (95% CI: 0. 585, 0. 685) vs. 0. 733 (95% CI: 0. 693, 0. 773) ; validation 0. 833 (95% CI: 0. 787, 0. 879) vs. 0. 589 (95% CI: 0. 537, 0. 641) vs. 0. 641 (95% CI: 0. 590, 0. 692) ; P < 0. 050). Therefore, the combined model provided the most accurate identification of LNM. Conclusion The SVM model incorporating 3D (R) features, 3D (C) features, and clinical risk factors effectively predicts preoperative LNM in CRC patients.
Guo et al. (Fri,) studied this question.