Value of dual-layer spectral detector CT in predicting lymph node metastasis of non-small cell lung cancer

Abstract

Background The accurate assessment of lymph node metastasis (LNM) is crucial for the staging, treatment, and prognosis of lung cancer. In this study, we explored the potential value of dual-layer spectral detector computed tomography (SDCT) quantitative parameters in the prediction of LNM in non-small cell lung cancer (NSCLC). Methods In total, 91 patients presenting with solid solitary pulmonary nodules (8 mm < diameter ≤30 mm) with pathologically confirmed NSCLC (57 without LNM, and 34 with LNM) were enrolled in the study. The patients’ basic clinical data and the SDCT morphological features were analyzed using the chi-square test or Fisher’s exact test. The Mann-Whitney U -test and independent sample t -test were used to analyze the differences in multiple SDCT quantitative parameters between the non-LNM and LNM groups. The diagnostic efficacy of the corresponding parameters in predicting LNM in NSCLC was evaluated by plotting the receiver operating characteristic (ROC) curves. A multivariate logistic regression analysis was conducted to determine the independent predictive factors of LNM in NSCLC. Interobserver agreement was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. Results There were no significant differences between the non-LNM and LNM groups in terms of age, sex, and smoking history. Lesion size and vascular convergence sign differed significantly between the two groups (P<0.05), but there were no significant differences in the six tumor markers. The SDCT quantitative parameters [SAR 40keV , SAR 70keV , Δ 40keV , Δ 70keV , CER 40keV , CER 70keV , NEF 40keV , NEF 70keV , λ, normalized iodine concentration (NIC) and NZ eff ] were significantly higher in the non-LNM group than the LNM group (P<0.05). The ROC analysis showed that CER 40keV , NIC, and CER 70keV had higher diagnostic efficacy than other quantitative parameters in predicting LNM [areas under the curve (AUCs) =0.794, 0.791, and 0.783, respectively]. The multivariate logistic regression analysis showed that size, λ, and NIC were independent predictive factors of LNM. The combination of size, λ, and NIC had the highest diagnostic efficacy (AUC =0.892). The interobserver repeatability of the SDCT quantitative and derived quantitative parameters in the study was good (ICC: 0.801–0.935). Conclusions The SDCT quantitative parameters combined with the clinical data have potential value in predicting LNM in NSCLC. The size + λ + NIC combined parameter model could further improve the prediction efficacy of LNM.

Dual-layer detector spectral CT-based machine learning models in the differential diagnosis of solitary pulmonary nodules

“…Derived parameters were calculated based on the above values, including SAR VNC , SAR 40 keV , SAR 70 keV , Δ 40 keV , Δ 70 keV , Δ SA_40 keV , Δ SA_70 keV , CER 40 keV , CER 70 keV , NEF 40 keV , NEF 70 keV and λ HU . The calculation formulas of the above derived parameters were as follows 30 – 34 : …”