ArticleQuantitative imaging in medicine and surgery2026
Diagnostic performance of spectral CT quantitative parameters in predicting spread through air spaces of lung adenocarcinoma.
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: Spread through air spaces (STAS) is associated with adverse outcomes in lung adenocarcinoma (LUAD). Although conventional computed tomography (CT) features have shown predictive value for STAS, the incremental diagnostic value of spectral CT quantitative parameters remains unclear. This study aimed to evaluate the diagnostic performance of spectral CT in predicting STAS in solid and part-solid nodules. Methods: A total of 164 patients with surgically confirmed LUAD presenting as solid and part-solid nodules who underwent dual-phase spectral CT scans were included. Conventional CT features (e.g., consistency, margin, spiculation) and spectral CT parameters [e.g., monochromatic CT values at 100 keV, normalized iodine concentration (NIC), effective atomic number (Zeff), and energy attenuation curve slope (λ)] were extracted. Three multivariate logistic regression models: conventional CT model, spectral CT model, and an integrated model were constructed. Model performance was assessed using the area under the curve (AUC), and AUCs were compared using DeLong test. Results: In the multivariable study, lobulation [odds ratio (OR) =3.509, 95% confidence interval (CI): 1.059-11.627], whole tumor size (OR =1.060, 95% CI: 1.013-1.110), and consolidation-to-tumor ratio (CTR; OR =72.057, 95% CI: 14.619-355.176) were significant conventional CT predictors, whereas arterial λ (OR =0.578, 95% CI: 0.396-0.846) and CT number at venous 100 keV (OR =1.017, 95% CI: 1.009-1.024) were significant spectral CT predictors of STAS positivity. The integrated model achieved the numerically highest AUC (0.842, 95% CI: 0.777-0.894), followed by the conventional CT model (AUC =0.829, 95% CI: 0.763-0.883) and the spectral CT model (AUC =0.829, 95% CI: 0.762-0.883). However, the differences among the three radiological models were not statistically significant (all P>0.05). Conclusions: Spectral CT parameters provided diagnostic performance comparable to that of conventional CT features for predicting STAS in solid and part-solid nodules. However, adding spectral CT parameters to conventional CT features did not yield a statistically significant incremental diagnostic value in this cohort.
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