Abstract

Background: Pulmonary mass-like lesions are one of the common manifestations of respiratory disorders. Differential diagnosis of these lesions is a major challenge in imaging studies. Objectives: This study aimed to explore the efficacy of spectral computed tomography (CT) features combined with conventional CT features in the differential diagnosis of pulmonary mass-like lesions. Patients and Methods: This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. The imaging characteristics and spectral CT parameters were evaluated in 77 patients who met the inclusion criteria. A multivariate logistic regression analysis was performed to determine independent predictors of malignant pulmonary lump-like lesions. Three models were established, including a radiomic feature model, a spectral CT model, and a combined model. A receiver operating characteristic (ROC) curve was also plotted to evaluate the diagnostic efficiency of the models. Results: Some CT features were significantly different between the malignant and benign groups, including the long-axis diameter (44.86 ± 18.42 in the malignant group vs. 55.59 ± 22.57 in the benign group; P = 0.07), mediastinal lymphadenopathy (25.00% in the benign group vs. 62.26% in the malignant group; P = 0.02), and mediastinal lymph node confluence (4.17% in the benign group vs. 41.51% in the malignant group; P = 0.01). The CT values at 40 keV (157.25 ± 79.23 vs. 148.46 ± 25.36, P = 0.047) and K40 - 70 keV (2.76 ± 2.05 vs. 2.52 ± 0.60, P = 0.04) were significantly higher in the benign group compared to the malignant group in the arterial phase (AP). Besides, the iodine concentration (IC) (14.73 ± 10.65 vs. 13.44 ± 3.24, P = 0.039; 17.52 ± 5.29 vs. 13.87 ± 5.81, P = 0.035), normalized iodine concentration (NIC) (0.15 ± 0.06 vs. 0.11 ± 0.05, P = 0.015; 0.41 ± 0.11 vs. 0.35 ± 0.10, P = 0.017), and Zeff value (8.46 ± 0.63 vs. 8.43 ± 0.28, P = 0.034; 8.60 ± 0.29 vs. 8.39 ± 0.33, P = 0.035) were significantly higher in the benign group compared to the malignant group, both in the AP and venous phase (VP). The logistic regression model, integrating CT features and spectral CT parameters, showed the highest diagnostic efficacy (area under the curve [AUC], 0.956; sensitivity, 87.5%; specificity, 90.6%). Conclusion: The quantitative spectral CT parameters, combined with conventional CT features, could help distinguish benign and malignant pulmonary mass-like lesions, providing an essential basis for developing treatment plans.

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