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

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.