Efficacy of an artificial intelligence-assisted diagnostic system and Lung-RADS in predicting the benignity and malignancy of pulmonary nodules with different clinical characteristics
TANG Yalun
LI Rui
GAO Lei
CAO Yang
QIAO Bingli
LIU Dianna
JIANG Min
ZHANG Yipeng
HU Kaiwen
Abstract：Objective To evaluate the effectiveness of an artificial intelligence(AI)image-assisted diagnostic system in the prediction of pulmonary nodules and its clinical application value.Methods A total of 212 patients with definitive pathologic diagnoses of pulmonary nodules underwent analysis of their preoperative chest CT images,which were provided in DICOM format,using the AI-assisted diagnostic system.The diagnostic effectiveness of the AI model and Lung-RADS were compared in predicting of benign and malignant pulmonary nodules with different clinical and imaging characteristics.Results The AI model demonstrated higher diagnostic accuracy than Lung-RADS in distinguishing between benign and malignant pulmonary nodules(70.75%vs 60.85%,P<0.05).Results of the stratified analysis were as follows.By age:The AI model showed higher accuracy than Lung-RADS for patients aged 50-59 years(70.31%vs 53.13%,P<0.05).By nodule position:There were no significant differences between he AI model and Lung-RADS(P>0.05).By nodule density:The AI model showed higher accuracy than Lung-RADS for the mixed-ground glass nodules(74.51%vs 49.02%,P<0.05).By nodule size:The AI model showed higher accuracy than Lung-RADS for the nodules measuring 10-19 mm in diameter(74.75%vs 66.67%,P<0.05).By malignant pathology:The AI model exhibited higher accuracy in predicting adenocarcinoma nodules compared to Lung-RADS(77.52%vs 62.79%,P<0.05).Conclusion The AI image-assisted diagnostic system surpasses Lung-RADS in assessing the benign and malignant pulmonary nodules.With ongoing technological advancements,it has the potential to provide a reliable foundation for the early,non-invasive diagnosis of pulmonary nodules.
Keywords：lung nodulesartificial intelligenceimage-assisted diagnostic systemLung-RADSchest CT
Publication Date：2025-06-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 668-677 )