The added value of quantitative contrast-enhanced CT parameters in distinguishing malignant from benign solid pulmonary nodules.
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).
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 (AVN), value of arterial phase (AVA), value of venous phase (AVV), their differentials (△AVA-N, △AVV-N, △AVV-A), 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).
Malignant SPNs showed higher age and greater AVA, AVN, AVV, △AVA-N, △AVV-N, diameter (P < 0.05). AVV 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 AVV. 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).
Quantitative CECT parameters, particularly AVV, aid in discriminating malignant SPNs. Combining AVV with qualitative features enhances diagnostic accuracy for malignancy risk assessment.
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 (AVN), value of arterial phase (AVA), value of venous phase (AVV), their differentials (△AVA-N, △AVV-N, △AVV-A), 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).
Malignant SPNs showed higher age and greater AVA, AVN, AVV, △AVA-N, △AVV-N, diameter (P < 0.05). AVV 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 AVV. 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).
Quantitative CECT parameters, particularly AVV, aid in discriminating malignant SPNs. Combining AVV with qualitative features enhances diagnostic accuracy for malignancy risk assessment.