Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors

Purpose Noninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist precise treatment strategies. The aim of this study was to explore machine learning (ML)-based peritumoral ultrasound radiomics signature (PURS), compared with intratumoral radiomics (IURS) and clinicopathologic factors, for early prediction of pCR. Methods We analyzed 358 locally advanced breast cancer patients (250 in the training set and 108 in the test set), who accepted NAC and post NAC surgery at our institution. The PURS and IURS of baseline breast tumors were extracted by using 3D-slicer and PyRadiomics software. Five ML classifiers including linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and adaptive boosting (AdaBoost) were applied to construct radiomics models for the prediction of pCR. The performance of PURS, IURS models and clinicopathologic predictors were assessed with respect to sensitivity, specificity, accuracy and the areas under the curve (AUCs). Results For the PURS models, the RF classifier achieved better efficacy (AUC of 0.889) than LR (0.849), AdaBoost (0.823), SVM (0.746) and LDA (0.732) in the test set. For the IURS models, the RF classifier also obtained a maximum AUC of 0.931 than 0.920 (AdaBoost), 0.875 (LR), 0.825 (SVM), and 0.798 (LDA) in the test set. The RF-based PURS yielded higher predictive ability (AUC, 0.889; 95% CI: 0.814, 0.947) than clinicopathologic factors (AUC, 0.759; 95% CI: 0.657, 0.861; p < 0.05), but lower efficacy compared with IURS (AUC, 0.931; 95%CI: 0.865, 0.980; p < 0.05). Conclusion The peritumoral US radiomics, as a novel potential biomarker, may be a promising clinical approach to guide precise therapy decisions.