The predictive value of MRI imaging omics combined with clinical features in machine learning models for colorectal cancer liver metastasis
10.7659/j.issn.1005-6947.240611
- VernacularTitle:MRI影像组学结合临床特征的机器学习模型对结直肠癌肝转移的预测价值
- Author:
Bo LI
1
;
Guannan LIU
1
Author Information
1. 河南省南阳市第一人民医院 磁共振诊断室,河南 南阳 473000
- Publication Type:Journal Article
- Keywords:
Colorectal Neoplasms;
Neoplasm Metastasis;
Liver;
Multimodal Magnetic Resonance Imaging;
Imaging Omics;
Support Vector Machine;
Random Forests
- From:
Chinese Journal of General Surgery
2025;34(7):1410-1420
- CountryChina
- Language:Chinese
-
Abstract:
Background and Aims:Colorectal cancer liver metastasis(CRCLM)is a major cause of poor prognosis in patients with colorectal cancer.Accurate and noninvasive preoperative diagnosis is essential for treatment planning.Conventional clinical biomarkers have limited specificity.This study aimed to develop an efficient predictive model for CRCLM by integrating multimodal MRI imaging omics features with machine learning algorithms,and to evaluate its clinical value.Methods:A total of 150 patients with colorectal cancer who underwent preoperative MRI and were pathologically confirmed at Nanyang First People's Hospital between May 2022 and May 2024 were retrospectively analyzed.Patients were randomly divided into a training set(n=120)and a validation set(n=30),including 57 cases with CRCLM and 93 cases without.Univariate and multivariate analyses were performed to identify independent risk factors for CRCLM and to construct a clinical diagnostic model.Radiomics features were extracted from multimodal MRI,and the least absolute shrinkage and selection operator(LASSO)method was used for feature selection.Logistic regression(LR),support vector machine(SVM),and random forest(RF)models were built and compared for diagnostic performance.A combined clinical-imaging omics model was further established,and its performance and clinical utility were assessed using receiver operating characteristic curves and decision curve analysis(DCA).Results:Carcinoembryonic antigen(OR=1.323,95%CI=1.079-1.567),carbohydrate antigen 19-9(OR=2.512,95%CI=1.225-3.799),and neutrophil-to-lymphocyte ratio(OR=1.881,95%CI=1.354-2.409)were identified as independent risk factors for CRCLM(all P<0.05).The clinical model constructed with these three factors achieved an AUC of 0.793.Among radiomics models,the RF model demonstrated the highest AUC in both training and validation sets(0.770 and 0.763),outperforming LR and SVM.The combined RF-based model yielded AUC of 0.913 and 0.947 in the training and validation sets,respectively,significantly exceeding the performance of the clinical or imaging omics models alone.DCA confirmed the superior net clinical benefit of the combined model.Conclusion:The RF model showed the best diagnostic performance among imaging omics models.When integrated with clinical features,the combined RF model significantly improved the noninvasive diagnostic efficacy of CRCLM and demonstrated high potential for clinical application.
