Integrating peritumor and tumor CT radiomics features in predicting local control after SBRT in patients with pulmonary oligometastases - Yao Lu; Yiqi Wang; Yuxi Ding; Danni Chen; Wenguang He; Weixiang Zhong; Jing Yang; Senxiang Yan; Ge Ren; Feng Zhao (Radiation Oncology)
Abstract Purpose Local control prediction for patients with pulmonary oligometastases underwent stereotactic body radiotherapy (SBRT) is crucial for optimizing therapeutic strategies. This study aims to develop and validate a predictive radiomics model integrating both tumor-intrinsic and peritumoral features along with clinical factors to enhance local control prediction using a multi-center dataset. Materials and methods We analyzed 223 tumors from 146 patients, which was divided into a training set (n = 165) and an external validation set (n = 58). Radiomic features from the gross tumor volume (GTV) and peritumoral regions (pGTV) representing the tumor microenvironment (TME) in CT images were extracted and combined with clinical factors to build a clinical outcome prediction model. Tumor response was classified into Favorable Response Group (FRG) and Unfavorable Response Group (URG) according to the 3-month and 1-year follow-up. Models were built using a Multilayer Perceptron (MLP) approach with SHAP analysis. Results Model-G (with GTV features) and Model-P (with pGTV features) achieved a validation area under curve (AUC) of 0.806 and 0.708, respectively. Meanwhile, Model-GP (with GTV and pGTV features) demonstrated an improved performance with a validation AUC of 0.851, reflecting the added value of peritumoral features. The Model-GPC, which incorporated GTV, pGTV, and clinical features, achieved a best validation AUC of 0.902, demonstrating the model’s ability to robustly integrate clinical and radiomic data for accurate local control prediction. Conclusion The Model-GPC, integrating clinical and radiomic features, accurately predicts post-SBRT local control in pulmonary oligometastases. Incorporating peritumoral features and SHAP analysis enhances prediction accuracy, offering insights to optimize SBRT strategies.