ArticleAmerican journal of translational research2026
Predicting invasiveness of subsolid nodules: a HRCT-based model for lung adenocarcinoma.
Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSubsolid nodules (SSNs) pose a diagnostic challenge in lung adenocarcinoma management. High-resolution computed tomography (HRCT) allows detailed characterization of SSNs, aiding in distinguishing pathological subtypes.
objectiveTo investigate the correlation between HRCT features and pathological subtypes of SSNs, and their association with nodule size and morphological features.
methodsClinical and HRCT data from 84 patients with surgically confirmed lung adenocarcinoma were retrospectively analyzed. All patients underwent preoperative CT scans, with lesions measuring ≤3.0 cm and a ground-glass opacity component ≥50%. The evaluated CT characteristics included leison size, lobulation, spiculation, pleural indentation, and CT values. Pathological diagnosis were established according to the latest classification standards.
resultsSignificant differences were observed among AIS, MIA, and IAC groups in age, lobulation, spiculation, and nodule size (all P<0.01). IAC showed larger size (90.9% between 8-10 mm) and more aggressive features than AIS (57.6% ≤8 mm). Advanced vascular and bronchial patterns were associated with invasive subtypes (P<0.001). EGFR+ tumors exhibited larger size and higher CT values. Multivariate analysis identified age ≥55, lesion diameter ≥8.51 mm, and bronchial pattern as significant predictors for distinguishing MIA from IAC.
conclusionHRCT features effectively reflect pathological invasiveness of SSNs and can assist in differentiating lung adenocarcinoma subtypes, providing valuable information for diagnosis and treatment planning.
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