2142 - Radiogenomics Models for Predicting Prognosis in Locally Advanced Non-Small Cell Lung Cancer Patients Undergoing Definitive Chemoradiotherapy

X. Song Jr^1,2, L. Li², and S. Yuan²; ¹School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, China, ²Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China

Purpose/Objective(s): The study aimed to construct a multi-omics model integrating baseline clinical data, computed tomography (CT) images and genetic information to predict the prognosis of dCRT in locally advanced non-small cell lung cancer (LA-NSCLC) patients. Materials/

Methods: The study retrospectively enrolled 105 stage III LA-NSCLC patients who had undergone dCRT. The pre-treatment CT images were collected, and the primary tumor was delineated as a region of interest (ROI) on the image using 3D-Slicer, and the radiomics features were extracted. The Least Absolute Shrinkage and Selection Operator (LASSO) was employed for dimensionality reduction and selection of features. Genomic information was obtained from the baseline tumor tissue samples. To construct a multi-omics model by combining baseline clinical data, radiomics and genomics features. The predictive performance of the model was evaluated by the area under the curve (AUC) of the Receiver operating characteristic (ROC) and the concordance index(C-index).
Results: The median follow-up time was 30.1 months, and the median progression-free survival (PFS) was 10.60 months. Four features were made to construct the radiomics model. Multivariate analysis demonstrated the Rad-score, KEAP1 and MET mutations were independent prognostic factors for PFS. The C-index of radiomics model, genomics model and radigenomics model all performed well in the training group (0.590 vs. 0.606 vs. 0.663) and the validation group (0.599 vs. 0.594 vs. 0.650).
Conclusion: The radiomics model, genomics model and radigenomics model can all predict the prognosis of dCRT for LA-NSCLC, and the radigenomics model is superior to the single type model.