Clinical Study of Artificial Intelligence in Imaging Diagnosis of False Positive Lesions of Pulmonary Nodules.

Abstract

To determine the accuracy of diagnosis of pulmonary nodules using artificial intelligence method. Observational study. Place and Duration of the Study: Department of Thoracic Surgery, Jinan Central Hospital, Jinan, China, from January 2020 to May 2021. An analysis of clinical characteristics exhibited by 32 patients initially diagnosed with malignant tumours through imaging (LDCT) and artificial intelligence (AI), was reclassified as having benign lesions following surgical intervention. Quantitative parameters were assessed, including CT mean value, kurtosis, skewness, solid ratio, and the ratio of length to short diameter, within a cohort of 32 benign patients juxtaposed with 58 patients diagnosed with lung cancer during the same time frame. The AI-derived parameters were subjected to Mann-Whitney U non-parametric test. A total of 32 benign pulmonary lesions were evaluated that were initially misdiagnosed as malignant prior to surgery. These lesions displayed an average length of (18.56 ± 12.16) mm, with the majority characterised as solid (68.8%). Notably, a substantial proportion of these lesions exhibited imaging features akin to malignant growths. The AI-derived quantitative parameters of the 32 benign cases and the 58 malignant cases revealed statistical significance in average CT value and solid ratio. However, statistical significance was not established for kurtosis, skewness, or the ratio of length to short diameter. The area under the Receiver Operating Characteristic (ROC) curve for average CT value and solid ratio stood at 0.71 and 0.705, respectively. Among the cases initially misdiagnosed as malignant yet subsequently identified as benign, a notable number of these instances were solid nodules, often resembling malignant lesions in imaging characteristics. There was moderate discriminatory capacity for average CT value and solid ratio, rendering them valuable tools for distinguishing between benign and malignant lesions within this particular cohort. This underscores their high diagnostic significance. Artificial intelligence, Benign lesions of lung, Lung cancer, Quantitative parameters, Postoperative.