Staging Modalities in Non-Small Cell Lung Cancer: Evidence-Based Guideline and Systematic Review by the Brazilian Group of Thoracic Oncology.

INTRODUCTION

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths, and accurate staging is essential to guide therapeutic decision-making and optimize patient survival. This guideline, developed by the Brazilian Group of Thoracic Oncology, provides evidence-based recommendations for NSCLC staging.

METHODS

Using the Grading of Recommendations, Assessment, Development, and Evaluation methodology, a multidisciplinary expert committee formulated clinical questions to conduct a systematic review evaluating the diagnostic accuracy of six staging methods, comparing these modalities against histological confirmation or clinical follow-up: computed tomography (CT), positron emission tomography-computed tomography (PET-CT), endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), and mediastinoscopy. Meta-analyses were performed using a bivariate random-effects model to estimate pooled sensitivity, specificity, and summary receiver operating characteristic (sROC) curves.

RESULTS

The combination of EBUS-TBNA and EUS-FNA provided the highest diagnostic accuracy for mediastinal staging (sROC 0.99), comparable to mediastinoscopy, which remains the surgical gold standard. Individually, EBUS-TBNA (sROC 0.98) and EUS-FNA (sROC 0.96) also demonstrated high accuracy. CT and PET-CT showed lower accuracy (sROC 0.73 and 0.87, respectively) and were associated with substantial rates of false-positive and false-negative results. Based on these findings, a stepwise diagnostic workflow can be recommended: initial noninvasive imaging with CT and PET-CT, followed by minimally invasive staging with EBUS-TBNA and EUS-FNA for patients with suspected mediastinal involvement. Mediastinoscopy should be reserved for cases with negative or inconclusive endosonography.

CONCLUSION

This evidence-based, resource-conscious diagnostic algorithm prioritizes diagnostic accuracy, patient safety, and cost-effectiveness, reinforcing the complementary role of multimodal staging to improve clinical outcomes.