Next-Generation Sequencing vs. Clinical-Pathological Assessment in Diagnosis of Multiple Lung Cancers: A Systematic Review and Meta-Analysis.

Abstract

Accurately distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastasis (IPM) is crucial for tailoring treatment strategies and improving patient outcomes. While molecular methods offer significant advantages over traditional clinical-pathological evaluations, they lack standardized diagnostic protocols and validated prognostic value. This study systematically compared the diagnostic and prognostic performance of molecular methods versus clinical-pathological evaluations in diagnosing multiple lung cancers (MLCs), specifically focusing on the impact of next-generation sequencing (NGS) parameters on diagnostic accuracy. A review of 41 studies encompassing 1266 patients revealed that two molecular methods, Mole1 (manually counting shared mutations) and Mole2 (bioinformatics-assisted clonal probability calculation), both demonstrated superior diagnostic accuracy and prognostic discrimination capabilities. Molecular assessment, particularly Mole1, effectively stratified prognosis for MPLC and IPM, leading to significantly improved disease-free survival (DFS: HR = 0.24, 95% CI: 0.15-0.39) and overall survival (OS: HR = 0.33, 95% CI: 0.18-0.58). Further analysis suggests that a minimal panel of 30-50 genes may be sufficient to effectively differentiate prognoses. Compared to Mole1, Mole2 demonstrated greater specificity and stability across various panels, achieving AUC values from 0.962 to 0.979. Clinical-pathological evaluations proved unreliable, not only failing to distinguish prognosis effectively but also exhibiting a potential misdiagnosis rate of 35.5% and 33.6% compared to the reference diagnosis. To improve both cost-effectiveness and diagnostic accuracy, bioinformatics-assisted molecular diagnostics should be integrated into multidisciplinary assessments, especially for high-risk cases where diagnostic errors are common.