中国医学创新2026，Vol.23Issue(15)：108-112,5.DOI:10.3969/j.issn.1674-4985.2026.15.023
基于深度学习的CAD系统结合低剂量CT在孤立性肺结节良恶性鉴别中的价值
Value of Deep Learning-Based CAD System Combined with Low-dose CT in Differential Diagnosis of Benign and Malignant Solitary Pulmonary Nodules
摘要
Abstract
Objective:To explore the value of deep learning-based computer-aided design(CAD)system combined with low-dose CT in differential diagnosis of benign and malignant solitary pulmonary nodules,to provide reference for early clinical screening and accurate diagnosis of lung cancer.Method:Totally 82 patients with solitary pulmonary nodules admitted to Yichun People's Hospital from January 2023 to June 2025 were retrospectively selected.All patients received low-dose CT scan and were confirmed by pathological examination.Their low-dose CT imaging data and clinical data were collected.With pathological results as the gold standard,the efficacy of deep learning-based CAD system in differential diagnosis of benign and malignant solitary pulmonary nodules was compared with that of traditional imaging diagnosis(film reading conducted by the attending doctor).The accuracy rates in differential diagnosis of nodules in different sizes,and at different sites was compared between the two methods.Result:Pathological diagnosis confirmed 62 benign cases and 20 malignant cases among the 82 patients.With pathological results as the gold standard,deep learning-based CAD system demonstrated higher sensitivity,specificity,positive and negative predictive values in differential diagnosis of benign and malignant pulmonary nodules compared to traditional imaging diagnosis,with no statistically significant differences(P>0.05).However,there was a statistically significant difference in accuracy rate between the two methods(P<0.05).From the perspective of size,there were 34 nodules smaller than 5 mm,46 nodules ranging from 5-10 mm,and 2 nodules greater than 10 mm.In terms of location,there were 46 nodules in the upper lobe,6 in the middle lobe,and 30 in the lower lobe.There was no statistically significant difference in the diagnostic accuracy rate between the two methods for nodules at different sites(P>0.05).For nodules sized 5-10 mm,deep learning-based CAD system demonstrated significantly higher diagnostic accuracy rate than traditional imaging diagnosis(P<0.05).For nodules in other sizes,no statistically significant difference in diagnostic accuracy rate was observed(P>0.05).Conclusion:Deep learning-based CAD system combined with low-dose CT is more efficient than traditional imaging diagnosis for identifying benign and malignant solitary pulmonary nodules,particularly for nodules sized 5-10 mm.关键词
低剂量CT/孤立性肺结节/深度学习/计算机辅助检测系统Key words
Low-dose CT/Solitary pulmonary nodule/Deep learning/Computer-aided detection system分类
医药卫生引用本文复制引用
肖宇,黄仕华,吴鸿波,张燕香,易玉涛..基于深度学习的CAD系统结合低剂量CT在孤立性肺结节良恶性鉴别中的价值[J].中国医学创新,2026,23(15):108-112,5.基金项目
江西省卫生健康委科技计划项目(202511183) （202511183）
