Machine learning model combining MRI radiomics and clinical features for predicting lymph node metastasis in rectal cancer patients
ZHANG Hui
GUAN Xingqun
ZHOU Cuiru
CAI Zhiping
HU Qiugen
Abstract：Objective To construct a machine learning model using multimodal MRI radiomics features and clinical characteristics to predict lymph node metastasis in rectal cancer patients.Methods A retrospective analysis was conducted on clinical data and MRI images of 223 rectal cancer patients treated at Shunde Hospital of Southern Medical University from May 2018 to May 2023.Tumor regions were delineated using 3D Slicer software,and radiomics features were extracted using PyRadiomics software.Univariate logistic regression and LASSO regression were used to screen features,and clinical,radiomics,and clinico-radiomics models were constructed and evaluated in the training cohort(n=157)and validation cohort(n=66)to evaluate their predictive performance for lymph node metastasis.Results In the training cohort,the AUC values for the clinical,radiomics,and clinico-radiomics models were 0.668,0.725,and 0.771,respectively.The radiomics model and clinico-radiomics model demonstrated good predictive performance in the validation cohort,with the radiomics model achieving an AUC of 0.780.Conclusion Machine learning models based on MRI radiomics and clinical features can effectively predict lymph node metastasis in rectal cancer.Radiomics features provide high predictive value,while clinical features offer limited predictive value.
Keywords：rectal cancerlymph node metastasisradiomicsmultimodal MRImachine learning model
Publication Date：2025-09-20
Online Publishing Date：2025-10-22(First online date of this platform, not the publication date of the document)
Pages：5( 1163-1167 )