Evaluation of a dual-energy computed tomography parameter –radiomics combined model for solitary pulmonary nodules

作者：Xiaojing Kan, Bin Nan, Ruidi Ni, Congyan Wang, Yukun Pan, Doudou Liu, Yinghui Ge · 发表于：Frontiers in Oncology · 年份：2026 · DOI：10.3389/fonc.2026.1778055 · 研究领域：Advanced X-ray and CT Imaging、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

Objective To construct a dual-energy computed tomography (DECT) parameter–radiomics combined model based on DECT quantitative parameters and radiomic features and to evaluate its diagnostic performance in differentiating benign from malignant solitary pulmonary nodules. Materials and methods We retrospectively collected data from inpatients who underwent contrast-enhanced thoracic DECT scans at our hospital between June 2018 and November 2024. Quantitative parameters measured and calculated included iodine concentration, effective atomic number, normalized iodine concentration, normalized effective atomic number, spectral Hounsfield unit slope, and extracellular volume fraction. These DECT parameters were used to construct a predictive model. The dataset was randomly divided into training and test sets in a 7:3 ratio. Radiomic features were extracted from mixed-energy images of the arterial and venous phases (A 120kVp and V 120kVp ) and from 70-keV monoenergetic images (A 70keV and V 70keV ). Six machine-learning algorithms were used to construct radiomics models, and the optimal model was selected based on performance. This optimal radiomics model was then combined with the DECT parameter model to create the combined model. Model performance was evaluated using accuracy, sensitivity, specificity, and area under the curve (AUC), and model comparisons were performed using the DeLong test (Python). A P -value of<0.05 was considered statistically significant. Results In t...