The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening

作者：Xiaoqing Lin, Ziwei Zhang, Taohu Zhou, Jie Li, Qianxi Jin, Yueze Li, Yu Guan, Yi Xia, Xiuxiu Zhou, Li Fan · 发表于：International Journal of COPD · 年份：2025 · DOI：10.2147/copd.s508775 · 被引用次数：4 · 研究领域：Lung Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Chronic Obstructive Pulmonary Disease (COPD) Research

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Comorbidities in patients with COPD significantly increase morbidity, mortality, and healthcare costs, posing a significant burden on the management of COPD. Given the complex clinical manifestations and varying severity of COPD comorbidities, accurate diagnosis and evaluation are particularly important in selecting appropriate treatment options. With the development of medical imaging technology, AI-based chest CT, as a noninvasive imaging modality, provides a detailed assessment of COPD comorbidities. Recent studies have shown that certain radiographic features on chest CT can be used as alternative markers of comorbidities in COPD patients. CT-based radiomics features provided incremental predictive value than clinical risk factors only, predicting an AUC of 0.73 for COPD combined with CVD. However, AI has inherent limitations such as lack of interpretability, and further research is needed to improve them. This review evaluates the progress of AI technology combined with chest CT imaging in COPD comorbidities, including lung cancer, cardiovascular disease, osteoporosis, sarcopenia, excess adipose depots, and pulmonary hypertension, with the aim of improving the understanding of imaging and the management of COPD comorbidities for the purpose of improving disease screening, efficacy assessment, and prognostic evaluation.