Objective To explore the predictive value of machine learning based on T2WI tumor and mesorectum for lymph node metastasis of rectal cancer after neoadjuvant therapy.Methods A retrospective analysis was conducted on the clinical imaging data and postoperative pathological data of 171 cases of rectal cancer before neoadjuvant therapy.According to postoperative pathological results,it was divided into N-(N0)and N+(N1,N2).Statistical analysis screened clinical features(laboratory indicators and MRI imaging findings)that were correlated with N staging(pN).The ITK-SNAP soft-ware was used to manually draw the whole tumor focus of rectal cancer as the region of interest(ROI-1)and the mesorectal region as the ROI-2 on the high-resolution T2WI image of rectal MRI before the new adjuvant treatment;Extract all imaging omics features from ROI-1 and ROI-2,retaining features with good stability(ICC ≥ 0.75).Using the Least Absolute Shrinkage and Selection Operator(LASSO)method,the most relevant features to pN were selected from ROI-1,ROI-2,and fusion features(ROI-1+ROI-2+clinical features).The four groups of features selected were used to construct pN predic-tion models using seven machine learning algorithms,namely,support vector machine(SVM),K-nearest neighbor algorithm(KNN),Random forest(RF),extreme Random tree(ET),gradient lifting decision tree(XGBoost),LightGBM(LGBM),and Logistic regression(LR).Results Among the 171 rectal cancer patients,the surgical results showed 92 cases in the N-group and 79 cases in the N+group.Among the seven models constructed based on ROI-1 screening,the LR model had the best performance,with AUC of 0.656,accuracy of 0.714,sensitivity of 0.583,and specificity of 0.783 in the test set.In ROI 2,the SVM model had the best performance,with corresponding AUC,accuracy,sensitivity,and specificity of 0.721,0.657,0.917,and 0.522 in the test set.In clinical models,the LR model had the best performance,with corresponding val-ues of 0.768,0.771,0.833,and 0.773.In the fusion model,the LR model had the best prediction performance,with corre-sponding values of 0.866,0.800,0.917,and 0.739.The LR model with fused models had the best predictive performance.Conclusion Histological analysis of MRI images of rectal tumor and mesorectum based on machine learning can predict the lymph node metastasis of rectal cancer after neoadjuvant treatment.The accuracy of the prediction model can be im-proved by fusing the two region and multi group characteristics and using Logistic regression method.
Magnetic resonance imagingImaging omicsRectal cancerMachine learningN-staging
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