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Predicting invasiveness of subsolid nodules: a HRCT-based model for lung adenocarcinoma
Feng Li1, Changhui Xue2, Yang Chen1
1Department of Medical Imaging, Nanping First Hospital Affiliated to Fujian Medical University Nanping 353000, Fujian, China.
American Journal of Translational Research
|February 12, 2026
Summary
High-resolution computed tomography (HRCT) features correlate with pathological subtypes of subsolid nodules (SSNs) in lung adenocarcinoma. These imaging findings aid in distinguishing invasive subtypes, improving diagnosis and treatment planning.
Area of Science:
- Pulmonary Medicine
- Radiology
- Oncology
Background:
- Subsolid nodules (SSNs) present diagnostic challenges in lung adenocarcinoma.
- High-resolution computed tomography (HRCT) aids in characterizing SSNs and differentiating pathological subtypes.
Purpose of the Study:
- To correlate HRCT features with pathological subtypes of SSNs.
- To assess the association between HRCT features, nodule size, and morphology in lung adenocarcinoma.
Main Methods:
- Retrospective analysis of HRCT and clinical data from 84 patients with surgically confirmed lung adenocarcinoma.
- Evaluation of CT characteristics including size, lobulation, spiculation, pleural indentation, and CT values.
- Pathological diagnosis based on the latest classification standards.
Main Results:
- Significant differences in age, lobulation, spiculation, and nodule size were found among adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups.
- IACs were larger and showed more aggressive features than AIS.
- Advanced vascular and bronchial patterns correlated with invasive subtypes; HRCT features predicted MIA vs. IAC differentiation.
Conclusions:
- HRCT features effectively reflect the pathological invasiveness of SSNs.
- HRCT aids in differentiating lung adenocarcinoma subtypes, providing valuable diagnostic and treatment planning information.
Keywords:
Chinese lung adenocarcinoma patientsHRCT (high-resolution computed tomography)Subsolid noduleslung adenocarcinomamicrovessel density EGFR mutationMore Related Videos
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