Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors

作者：Ya Li, Min Zhang, Yong Hu, Bo Du, Y. J. Mo, Tony He, Mingdan Zhao, Benlan Li, Xia Ji, Zhongjun Huang, Fangyang Lu, Zhen Huang, Bing Lu, Jie Peng · 发表于：Technology in Cancer Research & Treatment · 年份：2025 · DOI：10.1177/15330338251351109 · 被引用次数：4 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Medical Imaging Techniques and Applications

ObjectiveTo develop and validate a CT-based radiomics model for predicting sequential immunotherapy response after concurrent radiochemotherapy (CCRT) in patients with unresectable stage III non-small cell lung cancer (NSCLC).MethodsThe study retrospectively included 71 patients who received sequential immunotherapy after concurrent chemoradiotherapy (CCRT) between January 2019 and December 2022, while prospectively including 17 additional patients between January 2023 and July 2023. The study documented each patient's tumor response and prognosis within two months of completing treatment. Patients were then categorized based on their treatment response, resulting in the identification of two distinct groups: treatment-responsive group and treatment-insensitive group. First, ITK-SNAP software was used to delineate the primary tumor lesions in the lung window and define a region of interest (ROI). Second, features were extracted using Python (version 3.6) and filtered using Least absolute shrinkage and selection operator regression. Third, radiological models were built using six machine learning algorithms: logistic regression (LR), discriminant analysis (DA), neural network (NN), random forest (RF), support vector machine (SVM) and K-Nearest Neighbour (KNN). Finally, Kaplan-Meier survival analysis was performed for high- and low-risk patients predicted by radiomic modeling.ResultsBased on the performance of radiomics models constructed by various machine learning algorithms ...