The added value of quantitative contrast-enhanced CT parameters in distinguishing malignant from benign solid pulmonary nodules.

BMC Med Imaging

Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.

Published: July 2025


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Article Abstract

Purpose: To explore the added value of quantitative parameters derived from routine chest contrast-enhanced computed tomography (CECT) in distinguishing malignant from benign solid pulmonary nodules (SPNs).

Methods: Eighty-one SPNs pathologically confirmed as benign or malignant with preoperative nonenhanced chest and CECT scans were retrospectively analyzed. Quantitative parameters [CT attenuation value of nonenhanced phase (AV), value of arterial phase (AV), value of venous phase (AV), their differentials (△AV, △AV, △AV), diameter] and qualitative CT features [lobulation, spiculation, vacuolar sign, pleural depression sign, vascular convergence, edge clarity] were obtained. Inter-group comparisons for clinical/imaging variables used t-tests/Mann-Whitney U tests or Chi-square/Fisher's tests. Three multivariate logistic regression models (qualitative, quantitative, and combined models) were developed and evaluated through five-fold cross-validation, DeLong tests (Bonferroni-corrected α = 0.0167), decision/calibration curves, and Bootstrap-based threshold sensitivity analysis (1000 iterations; 0.1-0.9 thresholds). Subgroup ROC analyses assessed age/diameter effects (stratified by mean/median).

Results: Malignant SPNs showed higher age and greater AV AV, AV, △AV, △AV, diameter (P < 0.05). AV showed the strongest discriminatory power among quantitative parameters (AUC = 0.779). The qualitative model incorporated vascular convergence, pleural depression sign, and lobulation, while the quantitative model included AV. Cross-validation yielded mean AUCs of 0.877 ± 0.019, 0.790 ± 0.081, and 0.900 ± 0.042 for the qualitative, quantitative, and combined model respectively. The combined model surpassed the qualitative model (P = 0.016), demonstrating better calibration and decision curve performance. Bootstrap analysis identified 0.4 as the optimal sensitivity-specificity threshold. Subgroup AUCs were 0.945/0.860 (mean-age strara) and 0.903/0.912 (median-diameter strata).

Conclusions: Quantitative CECT parameters, particularly AV, aid in discriminating malignant SPNs. Combining AV with qualitative features enhances diagnostic accuracy for malignancy risk assessment.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12288267PMC
http://dx.doi.org/10.1186/s12880-025-01832-9DOI Listing

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