Machine Learning with Multiparametric MRI-based Radiomics Models for Preoperative Prediction of Ki-67 Status in Luminal Breast Cancer

Abstract

Abstract Background The main objective of the study was to determine whether multiparametric MRI (mpMRI) radiomics models supported by machine learning could preoperatively predict Ki-67 status in luminalbreast carcinoma. Methods Between 2018 and 2021, patients with luminal breast cancer who underwent mpMRI in our institution were retrospectively enrolled. The Ki-67 status was analyzed by biopsy preoperatively. Radiomics features were extracted from the T2WI, DCE, DWI, and ADC images, and mpMRI features were derived from four MRI sequences. A prediction model was developed by training the logistic regression (LR), multilayer perceptron (MLP), random forest (RF), and support vector machine (SVM) machine learning classifiersaccording to the radiomic characteristics. A clinical-radiomic nomogram was constructed by integrating mpMRI radiomic features and routine clinical MRI variables, followed by calibration and decision curve analyses. Results A total of 140 patients (85 with high and 55 with low Ki-67 expression) were enrolled. Compared to the DCE-, DWI-, and ADC-based radiomic signatures, the T2WI-based radiomic signature exhibited high prediction quality with AUCs of 0.87, 0.92, 0.92, and 0.89 for the four classification algorithms (LG, RF, MLP, SVM), respectively (all p<0.05). The mpMRI radiomic signature also showed high quality with AUCs of 0.92, 0.89, 0.92, and 0.92 for the four algorithms (all p<0.05). A prediction clinical-radiomicnomogram was constructed with training and validation set AUCs of 0.93 (0.90-0.96) and 0.92 (0.89-0.95), respectively. Conclusion T2-based and mpMRI-based radiomics models combined with advanced machine learning classifiers could assist in the preoperative individual-specific prediction of Ki-67 status in luminalbreast carcinoma.