This study aims to evaluate the diagnostic efficacy of multiparametric analysis using dual-layer spectral CT(DLCT) in distinguishing between small pulmonary oligometastases and benign lung nodules, with diameters ranging from 3 mm to 10 mm. A retrospective analysis was conducted on 72 patients with a history of primary malignancy and suspected pulmonary oligometastases, confirmed via surgical biopsy, pathology, and clinical follow-up. Collected data included virtual monochromatic images (VMI), spectral attenuation curve slopes ( λHu), iodine concentration (IC), normalized iodine concentration (NIC), and the effective atomic number (Zeff). Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves, with area under the curve (AUC) calculations to determine the diagnostic efficacy of DLCT in distinguishing between benign and malignant nodules, and to identify key parameters and their optimal cutoff values. The multiparametric analysis using DLCT demonstrated robust diagnostic performance in differentiating small pulmonary oligometastases from benign nodules. Metastatic nodules exhibited significantly greater long-axis diameter, short-axis diameter, and cross-sectional area compared to benign nodules (p < 0.001). Among DLCT parameters, small pulmonary oligometastases demonstrated significantly higher values in CT40keV-AP, CT40keV-VP, CT70keV-VP, IC, NIC, and Zeff than benign nodules (p < 0.001). CT40keV-VP reaching 89.47% sensitivity and VP-IC achieving 89.47% sensitivity. Notably, AP-λHu70 and AP-λHu100 exhibited balanced performance, with AUCs of 0.752 and 0.75, and specificity values of 70.59%. In conclusion, multiparametric DLCT analysis utilizing parameters such as λHu, IC, and Zeff enhances diagnostic accuracy for small pulmonary nodules, reduces unnecessary biopsies, and provides a reliable non-invasive tool for distinguishing oligometastases from benign lesions.
Qiu et al. (Thu,) studied this question.