Delta Radiomics Analysis for Prediction of Intermediary- and High-Risk Factors for Patients with Locally Advanced Cervical Cancer Receiving Neoadjuvant Therapy

Abstract

OBJECTIVE: This study aimed to assess the feasibility of using magnetic resonance imaging (MRI)-based Delta radiomics characteristics extrapolated from the Ax LAVA+C series to identify intermediary- and high-risk factors in patients with cervical cancer undergoing surgery following neoadjuvant chemoradiotherapy. MATERIALS AND METHODS: A total of 157 patients were divided into two groups, namely, those without high-risk factors and the absence or presence of any intermediary-risk factors (negative set; n = 75) and those with more than one high-risk factor or more than two intermediary-risk factors (positive set; n = 82). Radiomics characteristics were extracted using Ax-LAVA+C MRI sequences. The data was divided into training (n = 126) and test (n = 31) sets in an 8:2 ratio. The training set data features were selected using the Mann-Whitney U test and the Least Absolute Shrinkage and Selection Operator (LASSO) test. The best radiomics features were then analyzed to build a preoperative predictive radiomics model for predicting intermediary- and high-risk factors in cervical cancer. Three models—the clinical model, the radiomics model, and the combined clinic and radiomics model—were developed in this study utilizing the random forest Algorithm. The receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity were used to assess the predictive efficacy and clinical benefits of each model. RESULTS: Three models were developed in this study to predict intermediary- and high-risk variables associated with postoperative pathology for patients who underwent surgery after receiving neoadjuvant radiation. In the training and test sets, the AUC values assessed using the clinical model, radiomics model, and combined clinical and radiomics models were 0.76 and 0.70, 0.88 and 0.86, and 0.91 and 0.89, respectively. CONCLUSION: The use of machine learning algorithms to analyze Delta Ax LAVA+C MRI radiomics features can aid in the prediction of intermediary- and high-risk factors in patients with cervical cancer receiving neoadjuvant therapy.