YANG Zhuowen 1 , ZHENG Zhizhong 1 , LI Bin 1 , HUI Yiming 1 , LING Mingzhi 1 , DANG Jiying 2 , LI Suiyang 3 , ZHANG Chunjiao 4 , YANG Long 5 , SI Liang 6 , SONG Tieniu 1 , MENG Yuqi 1
  • 1. Department of Thoracic Surgery, The Second Hospital of Lanzhou University (Second Clinical Medical College), Lanzhou, 730030, P. R. China;
  • 2. Department of Thoracic Surgery, Liangzhou Hospital, Wuwei, 733000, Gansu, P. R. China;
  • 3. Department of Thoracic Surgery, Jingning County People's Hospital, Jingning, 743400, Gansu, P. R. China;
  • 4. Department of Thoracic Surgery, Jinchang People's Hospital, Jinchang, 737100, Gansu, P. R. China;
  • 5. Department of Thoracic Surgery, Affiliated Hospital of Gansu Medical College, Pingliang, 744000, Gansu, P. R. China;
  • 6. Department of Thoracic Surgery, The First People's Hospital of Longnan, Longnan, 746000, Gansu, P. R. China;
LI Bin, Email: leebin@lzu.edu.cn
Artificial intelligence; pulmonary nodule; lung neoplasms; pathological subtyping; predictive performance
10.7507/1007-4848.202504074
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Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological typing of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological typing of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate analysis showed that age, nodule type (subsolid pulmonary nodule), average density, burr sign, and vascular convergence sign were independent risk factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), burr sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The AUC value of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological typing were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians' prediction of lung nodule pathological typing indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological typing of lung nodules, especially in predicting lung nodule pathological typing, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

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