Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence

• Published in: Thoracic cancer 2024-12, Vol.15 (35), p.2500-2508
• Main Authors: Shi, Wensong, Hu, Yuzhui, Yang, Yulun, Song, Yinsen, Chang, Guotao, Qian, He, Wei, Zhengpan, Gao, Liang, Sun, Yingli, Li, Ming, Yi, Hang, Wu, Sikai, Wang, Kun, Mao, Yousheng, Ai, Siyuan, Zhao, Liang, Zheng, Huiyu, Li, Xiangnan
• Format: Article
• Language: English
• Subjects: Adenocarcinoma in Situ - diagnostic imaging; Adenocarcinoma in Situ - pathology; Adenocarcinoma of Lung - diagnosis; Adenocarcinoma of Lung - diagnostic imaging; Adenocarcinoma of Lung - pathology; Adult; Aged; Artificial Intelligence; Clinical decision making; Entropy; Female; Gender; Hospitals; Humans; Hyperplasia; Intervention; Kurtosis; lung adenocarcinoma in situ; Lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Male; Males; maximum 3D diameter; maximum CT value; Medical imaging; Medical prognosis; Middle Aged; Mortality; Original; Pathology; Physicians; Quantitative analysis; Skewness; Software; Standard deviation; Tomography, X-Ray Computed - methods
• ISSN: 1759-7706; 1759-7714; 1759-7714
• DOI: 10.1111/1759-7714.15447

Background With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis. Methods The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module. Parameters evaluated included demographic data and various CT imaging metrics to identify indicators for clinical application, focusing on the mean CT value's predictive value. Results Significant differences were found in several parameters between AAH and AIS, with nodule mass showing the highest predictive value. When comparing AIS to MIA, total nodule volume was the best predictor, followed by the maximum CT value. Conclusion The mean CT value has limited discriminative power for AIS diagnosis. Instead, the maximum CT value and maximum 3D diameter are recommended for clinical differentiation. Nodule mass and volume of solid components are strong indicators for differentiating AIS from AAH and MIA, respectively. Lung adenocarcinoma in situ (AIS) marks a critical juncture in lung cancer development, classified as stage 0 lung cancer, and represents the initial transition from normal to neoplastic cells. Accurate clinical diagnosis of lung AIS is essential for early detection and treatment, preventing tumor progression due to oversights and misdiagnoses, and enabling personalized clinical decision‐making tailored to its “indolent growth” characteristics. This article advocates the employment of the maximum 3D diameter and the maximum CT value as pivotal parameters for AIS of the lung from AAH and MIA. With the advancement of AI in the field of pulmonary, the acquisition of nodule mass and volume data will become easier, which will undoubtedly enhance the precision of AIS diagnosis.