Differentiating pulmonary sarcoidosis from mediastinal lymph node metastasis via conventional computed tomography...

Pulmonary sarcoidosis and mediastinal lymph node metastasis (MLNM) secondary to lung cancer frequently present with overlapping imaging features on chest computed tomography (CT), posing considerable diagnostic challenges. Although histopathological confirmation remains the gold standard, noninvasive CT-based strategies may provide supplementary information to aid in pretest probability estimation. This study evaluated the ability of a multivariate diagnostic framework comprising certain conventional quantitative CT parameters to differentiate pulmonary sarcoidosis from MLNM.

This retrospective, single-center study included 125 patients with histopathologically confirmed diagnoses who underwent standardized chest CT between January 2022 and December 2024, comprising 40 patients with pulmonary sarcoidosis and 85 with lung cancer involving MLNM. Three conventional CT features were measured for the single largest mediastinal lymph node per patient: short-axis diameter (mm), contrast enhancement value [in Hounsfield units (HU)], and anatomical nodal station classified according to the International Association for the Study of Lung Cancer (IASLC) lymph node station map. Baseline characteristics were compared via the independent samples t-test for continuous variables and the Chi-squared test for categorical variables. Three combinatorial multivariate logistic regression models were constructed and internally validated via repeated 10-fold cross-validation. Diagnostic performance was evaluated through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC), sensitivity, specificity, and Youden index being calculated for each model. The 95% confidence intervals (CIs) for AUCs were computed via the DeLong method. A two-sided P value <0.05 was considered statistically significant.

Significant differences were observed between the sarcoidosis and lung cancer groups in terms of age (51.16±12.85 vs. 64.24±11.26 years; t=-4.628; P<0.001), sex distribution (female: 70.0% vs. 22.3%; χ²=17.563; P<0.001), and lymph node imaging characteristics. Among individual CT parameters, contrast enhancement value achieved the highest discriminative performance (AUC =0.785; 95% CI: 0.745-0.825; sensitivity =66.3%; specificity =90.1%; optimal cutoff =30.501 HU), while that of anatomical nodal distribution alone was poor (AUC =0.505; 95% CI: 0.460-0.549). The inverse of short-axis diameter demonstrated modest performance (AUC =0.586; 95% CI: 0.543-0.629). The composite model integrating inverse short-axis diameter, enhancement value, and nodal location achieved the highest overall performance, with an AUC of 0.798 (95% CI: 0.749-0.868), a sensitivity of 66.2%, a specificity of 94.8%, and a Youden index of 0.610 (P<0.001).

Although the composite CT-based model demonstrated high specificity, the moderate sensitivity indicates that approximately one-third of malignant cases would not be correctly identified by imaging criteria alone. These CT-derived quantitative parameters are thus not a reliable substitute for histopathological confirmation. Patients with indeterminate mediastinal lymphadenopathy should be referred for tissue sampling-preferably via endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA)-regardless of CT imaging findings. The proposed model should be considered a supplementary, hypothesis-generating research tool.