A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma

Abstract

BackgroundThis study aimed to construct and assess a comprehensive model that integrates MRI-derived deep learning radiomics, functional imaging (fMRI), and clinical indicators to predict early efficacy of radiotherapy in nasopharyngeal carcinoma (NPC). MethodsThis retrospective study recruited NPC patients with radiotherapy from two Chinese hospitals between October 2018 and July 2022, divided into a training set (hospital I, 194 cases), an internal validation set (hospital I, 82 cases), and an external validation set (hospital II, 40 cases). We extracted 3404 radiomic features and 2048 deep learning characteristics from MRI modalities - T1WI, CE-T1WI, T2WI, and T2WI/FS. Additionally, both the ADC and its maximum (ADCmax) from DWI imaging, along with TBF and its maximum (TBFmax) from ASL imaging were derived. We used four classifiers (LR, XGBoost, SVM and KNN) and stacked algorithm as model construction methods. The receiver operating characteristic (ROC) curve (AUC) and decision curve analysis (DCA) was used to assess models. ResultsThe manual radiomics model leveraging XGBoost and the deep learning model based on KNN (the AUCs in the training set: 0.909, 0.823, respectively) showed better predictive efficacy than other machine learning algorithms. The stacked model that integrated MRI-based deep learning radiomics, fMRI, and hematological indicators, has a large improvement of AUC in the training set [0.984 (95%CI: 0.972 - 0.996)], the internal validation set [0.936 (95%CI: 0.885 - 0.987)], and the external validation set [0.959 (95%CI: 0.901-1)]. ConclusionsOur research has developed a clinical-radiomics integrated model based on MRI which can predict early radiotherapy response in NPC and provide guidance for personalized treatment.