CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

Xiuming Zhang; Wei Qiao; Zheng Kang; Chunhan Pan; Yan Chen; Kang Li; Wenrong Shen; Lei Zhang

doi:10.2147/IJGM.S368344

CT Features of Stage IA Invasive Mucinous Adenocarcinoma of the Lung and Establishment of a Prediction Model

Int J Gen Med. 2022 Jun 4:15:5455-5463. doi: 10.2147/IJGM.S368344. eCollection 2022.

Authors

Xiuming Zhang^#¹, Wei Qiao^#¹, Zheng Kang¹, Chunhan Pan¹, Yan Chen², Kang Li¹, Wenrong Shen¹, Lei Zhang¹

Affiliations

¹ Department of Radiology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
² Department of Pathology, Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research & The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.

^# Contributed equally.

Abstract

Objective: To investigate computed tomography (CT) features of stage IA invasive mucinous adenocarcinoma (IMA) of the lung and establish a predictive model.

Methods: Fifty-three lesions from 53 cases of stage IA IMA between January 2017 and December 2019 were examined, while 141 lesions from 141 cases of invasive non-mucinous lung adenocarcinoma (INMA) served as control cases. Univariate analysis was performed to compare differences in demographics and CT features between the two groups, and multivariate logistic regression analysis was performed to determine primary influencing factors of solitary nodular IMA. A risk score prediction model was established based on the regression coefficients of these factors, and receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive performance of the model.

Results: Univariate analysis showed that age, nodule type, maximum nodule diameter, tumor lung interface, lobulation, spiculation, air bronchogram or vacuolar signs, and abnormal vascular changes differed significantly between the two groups (p < 0.05). Compared to INMA, spiculation of IMA was relatively longer and softer. Multivariate logistic regression analysis showed that nodule type, indistinct tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes were the primary influencing factors. A prediction model based on the regression coefficients of these factors was established. ROC curve analysis indicated that the area under the curve was 0.882 (p < 0.05).

Conclusion: Compared to INMA, solitary peripheral stage IA nodular IMA were more common in older patients; they more frequently had indistinct tumor lung interface and air bronchogram or vacuolar signs on CT; spiculation was relatively longer and softer; our risk score prediction model based on nodule type, tumor lung interface, air bronchogram or vacuolar signs, and abnormal vascular changes was established with good predictive efficacy for solitary nodular IMA.

Keywords: influencing factors; invasive mucinous adenocarcinoma of the lung; risk score modeling.

Grants and funding

This work was supported by the Roentgen special fund for image research of Jiangsu Medical Association (SYH-32011500008 2021003) and the Jiangsu Cancer Hospital Young Talents Plan (Jiangsu China).