Combined Radiomics–Clinical Model to Predict Radiotherapy Response in Inoperable Stage III and IV Non-Small-Cell Lung Cancer

Abstract

Purpose: Radiotherapy is a promising treatment option for lung cancer, but patients’ responses vary. The purpose of the study was to investigate the potential of radiomics and clinical signature for predicting the radiotherapy sensitivity and overall survival of inoperable stage III and IV non-small-cell lung cancer (NSCLC) patients. Materials: This retrospective study collected 104 inoperable stage III and IV NSCLC patients at the Yunnan Cancer Hospital from October 2016 to September 2020. They were divided into radiation-sensitive and non-sensitive groups. We used analysis of variance (ANOVA) to select features and support vector machine (SVM) to build the radiomic model. Furthermore, the logistic regression method was used to screen out clinically relevant predictive factors and construct the combined model of radiomics–clinical features. Finally, survival was estimated using the Kaplan–Meier method. Results: There were 40 patients in the radiation-sensitive group and 64 in the non-sensitive group. These patients were divided into training set (73 cases) and testing set (31 cases) according to the ratio of 7:3. Nine radiomics features and one clinical feature were significantly associated with radiotherapy sensitivity. Both the radiomics model and combined model have good predictive performance (the areas under the curve (AUC) values of the testing set were 0.864 (95% confidence interval [CI]: 0.683-0.996) and 0.868 (95% CI: 0.689-1.000), respectively). Only platelet level status was associated with overall survival. Conclusion: The combined model constructed based on radiomics and clinical features can effectively identify the radiation-sensitive population and provide valuable clinical information. Patients with higher platelet levels may have a poor prognosis.