Correlation between Ki-67 expression and a machine learning model based on multi-parametric MRI radiomics in rectal can-cer
Objective To investigate the value of radiomics model based on mp-MRI in predicting Ki-67 expression in rectal cancer.Methods The clinical data of 97 patients with rectal adenocarcinoma in Anhui Provincial Cancer Hospital from January 2016 to July 2023 were retrospectively analyzed.Preoperative routine MRI and IVIM-DWI were performed.According to the postoperative pathological Ki-67 ex-pression status,the patients were divided into the low expression group(n=64)and the high expression group(n=33).Three different sequences(High resolution Ax-T2WI,dynamic enhanced balanced phase Sag LAVA-FLEX and IVIM-DWI)were used to extract 3D radiomics texture analysis feature parameters.According to the ratio of 7∶3,they were divided into a training group(n=67)and a test group(n=30).The training group was used for feature screening and radiomics model establishment,and the test group was used to verify the reliability of the established radiomics model.Pearson correlation,SelectPercentile and LASSO were used to complete the selection of the best predictive features.SGD,SVM and LR machine learning algorithms were used to construct the models based on the radiomics features,respectively,and 10-fold cross-validation was performed.The receiver operating characteristic(ROC)curve was plotted to evaluate the efficacy of the model in predicting Ki-67 expression level in rectal adenocarcinoma,and the DeLong test was used to compare the area under the curve(AUC).Result A total of 5 622 radiomics features were extracted from high-resolution Ax-T2WI,Sag LAVA-FLE and IVIM-DWI(b=800 s/mm2)for each patient.Six ra-diomics features were selected as the best predictive features to construct the model.The highest AUCs of SGD,SVM and LR were 0.867,0.853 and 0.884,respectively.The accuracy of SGD algorithm was 76%,which had the best prediction performance among the three models.Conclusion The multi-parametric MRI-based radiomics model has certain value in preoperative prediction of Ki-67 expression level in rec-tal adenocarcinoma.SGD has the best performance in predicting the expression level of Ki-67 in rectal adenocarcinoma.
Ki-67Rectal cancerMulti-parametric magnetic resonance imagingRadiomicsMachine learning
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