Radiomic Prediction of Locally Advanced Cervical Squamous Cell Carcinoma Response to Concurrent Chemoradiotherapy using MRI.

Abstract

The aim of this study is to assess the predictive efficacy of a radiomics model utilizing magnetic resonance imaging (MRI) for predicting locally advanced cervical squamous cell carcinoma response to concurrent chemoradiotherapy. We enrolled a cohort of 139 patients diagnosed with stage IIB to IV cervical squamous cell carcinoma, based on the 2018 FIGO classification, who underwent concurrent chemoradiotherapy and pre-/post-treatment MRI examinations. These patients were divided into complete response and partial response groups. Prior to the initiation of treatment, the areas of interest within the lesion were delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and enhanced T1-weighted imaging (T1WI) sequences, from which radiomic features were extracted. The group was randomly divided into training (n = 111) and validation (n = 28) sets (8:2 ratio) to identify the optimal features. Logistic regression models were constructed to predict treatment response, with distinct models based on the following imaging modalities: enhanced T1WI, T2WI, DWI, a combination of T2WI and enhanced T1WI (joint model 1), and a combination of T2WI, DWI, and enhanced T1WI (joint model 2). Model fitness and predictive performance were assessed using receiver operating characteristic curves, while the clinical applicability of the models was analyzed using decision curve analysis. A cumulative count of 2,264 radiomic features was derived from the region of interest in each imaging sequence. Subsequently, 18, 16, 15, 16, and 13 optimal features were selectively identified from the five models. These selected features were employed to formulate a radiomics model designed for the prediction of treatment response. These selected features were used to construct individual radiomics models aimed at predicting treatment response. Subsequently, all models achieved AUCs > 0.8 in the validation set, with Joint Model 2 demonstrating the highest performance (AUC = 0.939, 95% CI: 0.826-1; sensitivity = 0.773, specificity = 0.833). No significant differences were observed between Joint Model 2 and other models (P > 0.05). The MRI-based radiomics model has high potential in effectively predicting the efficacy of concurrent chemoradiotherapy for locally advanced cervical squamous cell carcinoma.