Development and validation of a nomogram integrating multi-dimensional clinical factors for predicting lung cancer-related mediastinal/hilar lymph node metastasis before endobronchial ultrasound-guided transbronchial needle aspiration.

Background: Accurate preoperative identification of lung cancer-related mediastinal/hilar lymph node (LN) metastasis in patients with chest computed tomography (CT)-detected lymphadenopathy is critical for endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and lung cancer treatment planning. Single clinical indicators lack sufficient accuracy for distinguishing benign from malignant lymphadenopathy, thus creating an urgent need for a comprehensive predictive tool integrating multidimensional routine clinical data. This study aimed to develop and validate a nomogram prediction model based on patient clinical characteristics, imaging features, serological markers, and endoscopic findings to predict the probability that mediastinal/hilar lymphadenopathy is attributable to lung cancer metastasis before EBUS-TBNA. Methods: Clinical data of patients with CT-detected mediastinal/hilar lymphadenopathy who underwent EBUS-TBNA at the Affiliated Hospital of Southwest Medical University (January 2024-June 2025) were retrospectively collected. Following inclusion and exclusion screening, 298 patients were enrolled and categorized into the malignant group (n=173, pathologically confirmed as lung cancer metastasis) and the benign group (n=125, no evidence of malignant tumors on pathology and during ≥6 months of follow-up). The total cohort was stratified by LN pathology (benign Results: Seven independent predictors were identified: smoking history, CT-suspected malignancy, bronchoscopy-suspected mucosal invasion, LN short-axis diameter, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and carbohydrate antigen 125 (CA125). The nomogram showed excellent discrimination (training set AUC =0.952, validation set AUC =0.932) and good calibration (Hosmer-Lemeshow test: training set, P=0.69; validation set, P=0.26). At optimal cut-offs, the sensitivity (81.0%/82.7%) and specificity (95.4%/94.7%) were high in both sets. DCA showed superior net clinical benefits over the extreme strategies. Conclusions: We developed a clinically applicable nomogram that integrates routine clinical, imaging, serological, and endoscopic indicators to predict lung cancer-related mediastinal/hilar LN metastasis in patients with CT-detected lymphadenopathy. Using readily available clinical data, this model achieves excellent performance and clinical utility without reliance on advanced imaging [e.g., positron emission tomography/computed tomography (PET/CT)] or molecular testing, supporting precise preoperative risk stratification to guide EBUS-TBNA decision-making. It is especially suitable for patients who refuse or are ineligible for high-cost, high-radiation advanced examinations due to economic burden, radiation concerns, or medical contraindications. Additionally, a simplified nomogram was constructed as a complementary screening tool for resource-limited settings, further extending the overall clinical applicability of our predictive approach.