ABSTRACT

Objective: Efficient diagnostic pathways in advanced non-small cell lung cancer (NSCLC) are crucial for timely treatment initiation and improved outcomes. This study evaluated the impact of diagnostic delays and the role of minimally invasive techniques in biomarker assessment and survival in a real-world clinical cohort. Methods: A retrospective cohort study was conducted involving 205 patients with advanced NSCLC diagnosed between January 2020 and December 2023. Diagnostic procedures included EBUS/EUS-B, transthoracic biopsy, and surgical biopsy. The time-to-diagnostic procedure, time-to-therapy, and survival were analyzed using multivariate models. Results: The time interval to the first diagnostic procedure independently predicted mortality (HR=1.66; p=0.016). EBUS and EUS-B achieved significantly shorter diagnostic times (median 8 and 5 days, respectively) compared to transthoracic (20.5 days) and surgical (24.5 days) biopsies. These endoscopic techniques were also associated with shorter time intervals to systemic therapy initiation (p=0.011). Minimally invasive approaches provided sufficient tissue for complete morphological, immunohistochemical, and molecular profiling in most cases, with no significant differences in adequacy among procedures. Patients with actionable mutations had a 44% lower mortality risk (HR=0.56; p=0.013), while high PD-L1 expression was associated with a 56% reduction in mortality risk (HR=0.44; p=0.003). Conclusions: Minimally invasive techniques, particularly EBUS and EUS-B, shortened diagnostic delays, ensured adequate biomarker sampling, and enabled earlier initiation of systemic therapy. Since the time-to-diagnosis was independently associated with survival, these approaches may have indirectly contributed to improved outcomes. Our findings highlight the importance of streamlining diagnostic pathways and expanding access to endoscopic methods to optimize care in advanced NSCLC.

Keywords: Non-small cell lung cancer; Diagnostic pathways; EBUS; EUS-B; Molecular profiling; Survival analysis.

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