Integration of dual-energy CT parameters and radiomics features for non-invasive prediction of α-SMA and CD8 + T cell in non-small cell lung cancer

作者：Nan Jiang, Yan Zhang, Gangfeng Li, Xiaoyan Qu, Wen-Xiu Wang, Rong Hou, Y. Ma, Yang Yang, Ying Yu, Guang-Bin Cui · 发表于：Frontiers in Medicine · 年份：2026 · DOI：10.3389/fmed.2026.1792692 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers、Advanced X-ray and CT Imaging

Background The non-invasive characterization of the tumor microenvironment (TME) is essential for stratifying non-small cell lung cancer (NSCLC) patients who may benefit from immunotherapy. This study investigates a novel approach by integrating dual-energy CT (DECT) parameters with radiomics to quantitatively assess stromal fibrosis (via α -SMA area) and CD8 + T-cell infiltration. Methods In this prospective study, 70 treatment-naive NSCLC patients were enrolled. Preoperative DECT scans were used to extract both DECT parameters and radiomics features. Corresponding surgical specimens were analyzed to determine the area percentage of α -SMA-positive stroma and the density of CD8 + T cells, with patients classified into high and low groups for each biomarker. After feature selection, models were constructed based on DECT parameters alone, radiomics features alone, and a combined feature set. Models were evaluated via 5-fold cross-validation. Results For predicting high α -SMA expression, the integrated model combining DECT parameters and radiomics features demonstrated superior performance (AUC: 0.766) compared to models using either modality alone (DECT AUC: 0.670; radiomics AUC: 0.703). In contrast, for predicting CD8 + T-cell density, the DECT-only model (AUC: 0.715) performed comparably to the radiomics model (AUC: 0.695), with no significant gain from integration. Key discriminating features, such as normalized iodine concentration for α -SMA and spectral slope of K40-70 ...