PET/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer - Yang, Liping; Chang, Jianfei; He, Xitao; Peng, Mengye; Zhang, Ying; Wu, Tingting; Xu, Panpan; Chu, Wenjie; Gao, Chao; Cao, Shaodong; Kang, Shi (Frontiers in Oncology, 2022)
Backgrounds: The aim of this study was to evaluate the clinical usefulness of radiomics signatures derived 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) for the early prediction of neoadjuvant chemotherapy (NAC) outcomes in patients with (BC). Methods: A total of 124 patients with BC who underwent pre-treatment PET-CT scanning and received NAC between December 2016 and August 2019 were studied. The dataset was randomly assigned in a 7:3 ratio to either the training or validation cohort. Primary tumor segmentation was performed, and radiomics signatures were extracted from each PET-volume of interests (VOI) and CT-VOI. Radiomics signatures associated with pathological treatment response were selected from within a training cohort (n = 85), which were then applied to generate different classifiers to predict the probability of pathological complete response (pCR). Different models were then independently tested in validation cohort (n = 39) regarding their accuracy, sensitivity, specificity and area under curve (AUC). Results: 35 patients (28.2%) had pCR to NAC. 12 features consisted of 5 PET-derived signatures, 4 CT-derived signatures and 3 clinicopathological variables were candidate for model’s development. The random forest (RF), k-Nearest Neighbors (KNN) and decision tree (DT) classifiers were established, which could be utilized to predict pCR to NAC with AUC ranging from 0.819 to 0.849 in validation cohort. Conclusions: The PET/CT-based radiomics analysis might provide efficient predictors of pCR in patients with BC, which could potentially be applied in clinical practice for individualized treatment strategy formulation.