Study of Imaging Analysis for Distinguishing Alveolar Collapse From Invasive Lesions in Early‐Stage Lung Adenocarcinoma

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Authors: Shinji Kaneda, Atsushi Ito, Daisuke Ito, Teruhisa Kawaguchi, Motoshi Takao, Koji Kawaguchi
Cited by: 0
References: 23

Abstract

BACKGROUND: Non-invasive lung adenocarcinoma may occasionally contain solid benign components, presenting challenges for making an accurate diagnosis using CT (computed tomography). Therefore, we investigated whether a three-dimensional (3D) volumetric analysis was more effective than CT in distinguishing the presence of invasive lesions in early-stage lung adenocarcinoma. MATERIALS AND METHODS: We retrospectively classified 161 patients preoperatively diagnosed with clinical stage IA1 into two groups: the non-invasive lesion group (50 patients) and the invasive lesion group (111 patients). We conducted a comparative analysis concerning the predictive performance of CT and a 3D volumetric analysis to identify invasive lesions. In addition, a histological review of the pre-invasive lesion group was performed to assess the pathological characteristics of the solid component, as observed on preoperative CT. RESULTS: A univariate analysis demonstrated that the consolidation diameter, consolidation/tumor (C/T) ratio, consolidation volume, and three-dimensional (3D)-C/T ratio were significant predictors of the presence of invasive lesions. A multivariate analysis identified the consolidation volume as an independent predictor (p = 0.045). Analyses of the receiver operating characteristics showed that the areas under the curve for the consolidation diameter, consolidation volume, C/T ratio, and 3D-C/T ratio were 0.702, 0.634, 0.747, and 0.742, respectively. In addition, a histological review revealed alveolar collapse in 89.2% (25 of the 28) of the preinvasive lesion group. CONCLUSIONS: Consolidation diameter, consolidation volume, and 3D-C/T ratio are more reliable predictors for distinguishing alveolar collapse from invasive lesions than the C/T ratio in early-stage lung adenocarcinoma.

Fields of Study & Subjects

Lung Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical Imaging Medical Imaging and Pathology Studies

Publication details

Journal / source
Thoracic Cancer
Volume
17
Issue
2
Pages
e70246–e70246
Publication date
1 January 2026
Language
EN