Application of artificial intelligence-based dual source CT scanning in the differentiation of lung adenocarcinoma in situ and minimally invasive adenocarcinoma

Lihong Liu; Zhihua Ni; Jian Zhang; Junsong Zhao; Jieyun Shen

doi:10.12669/pjms.40.3.8454

Application of artificial intelligence-based dual source CT scanning in the differentiation of lung adenocarcinoma in situ and minimally invasive adenocarcinoma

Pak J Med Sci. 2024 Jan-Feb;40(3Part-II):271-276. doi: 10.12669/pjms.40.3.8454.

Authors

Lihong Liu¹, Zhihua Ni², Jian Zhang³, Junsong Zhao⁴, Jieyun Shen⁵

Affiliations

¹ Lihong Liu, PhD. Department of Radiology, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China.
² Zhihua Ni, PhD. Department of Radiology, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China.
³ Jian Zhang, PhD. Department of Radiology, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China.
⁴ Junsong Zhao, PhD, Department of Radiology, Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai, China.
⁵ Jieyun Shen, PhD, Department of Radiology, Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China.

Abstract

Background and objective: Lung adenocarcinoma is the most common type of lung cancer with highly incidence and mortality. Due to the overlap of morphological features, it is difficult to distinguish clinically between preinvasive lesions (in situ adenocarcinoma, AIS) and invasive lesions (minimally invasive adenocarcinoma, MIA), which appear as ground glass cloudy nodules. This study was performed to probe the application value of artificial intelligence (AI)-based dual source CT scanning in the differentiation of AIS as well as MIA.

Methods: The clinical data of 136 patients in Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine from January 2019 to January 2022 were retrospectively analyzed. The accuracy of AI in distinguishing lung AIS (n=76) and MIA (n=60) were analyzed. The effectiveness of AI in detecting nodules and its diagnostic efficacy for AIS and MIA were explored.

Results: The proportion of patients with clear and regular lesion boundaries in AIS was higher than that in MIA. The mean lesion diameter of AIS patients was shorter than MIA patients. There was no difference in the CT value between AIS and MIA in the ground glass nodule density area of pure ground glass nodule and mixed ground glass nodule, but the CT value of the solid nodule density area in AIS was lower. The occurrence of pulmonary vascular abnormality, air bronchogram sign, and pleural depression in AIS patients were lower than MIA patients. The detection rate of AI for lung adenocarcinoma with nodule diameter ≤ 5 mm, complete solid nodules and ground glass nodules was significantly higher than radiologists. The sensitivity, specificity, positive prediction rate, negative prediction rate and accuracy of AI detection were significantly higher than radiologists.

Conclusion: AI-based dual source CT scanning can clearly show the morphological characteristics of lung adenocarcinoma, which is helpful for the differential diagnosis of lung AIS as well as MIA.

Keywords: Artificial intelligence; Dual source CT scanning; Lung adenocarcinoma in situ; Minimally invasive adenocarcinoma.