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Official PDF TranslationChinese Journal of New Drugs

Predictive Modeling of Clinical Outcomes in Non-Small Cell Lung Cancer Using Multi-Omics Data and Machine Learning

Authors: ZHANG Wei; LI Ming; WANG Fang; et al.

DOI: pub_80__articleID_231Status: Verified Translated Edition
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Key Findings in This Report

• • The integrated model achieved a C-index of 0.78 for OS and 0.75 for PFS, outperforming clinical-only models by +0.12, enabling more accurate risk stratification in NSCLC. • • High-risk patients (top quartile by risk score) had a median OS of 18.2 months vs. 42.6 months for low-risk (p < 0.001), demonstrating strong discriminative power for treatment decisions. • • A 15-gene immune-related signature predicted immune checkpoint inhibitor response with AUC = 0.82, facilitating patient selection for immunotherapy. • • Tumor mutation burden (TMB) and PD-L1 expression were confirmed as independent prognostic factors, with hazard ratios of 1.45 (95% CI: 1.20-1.75) and 1.32 (95% CI: 1.10-1.58), respectively, guiding combination therapy strategies.