ArticleQuantitative imaging in medicine and surgery2026
Advanced characterization and grading of invasive lung adenocarcinoma: integrative analysis with spectral CT and
Article in Quantitative imaging in medicine and surgery, 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
Background: Lung cancer is the leading cause of cancer-related mortality globally. The novel classification system for invasive lung adenocarcinoma (LUAD), which integrates predominant histologic and high-grade patterns, has been shown to correlate with prognosis. This study aimed to evaluate and compare the discriminative ability of spectral computed tomography (CT), Methods: A total of 135 patients with 144 lung nodules who underwent preoperative spectral CT were evaluated retrospectively. Of these, 55 patients with 60 lung nodules underwent additional PET/CT imaging. CT morphological features, spectral CT and PET/CT parameters of the tumors were compared between the high-grade group (grade 3) and the low-grade group (grade 1 and 2). Univariate and multivariate analyses were performed using binary logistic regression with spectral CT and PET/CT parameters. The diagnostic efficiencies of spectral CT parameters, PET metabolism parameters, and a combination of CT features were computed by receiver operating characteristic (ROC) curve analysis. The discriminative power and calibration of the nomogram were evaluated. Results: There were significant differences in morphological CT (nodule attenuation), spectral CT parameters [iodine concentration of the lesion in arterial phase (ICL Conclusions: The spectral CT model, including nodule attenuation type and ICL
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