ArticleFrontiers in immunology2025
Quality-of-life scale machine learning approach to predict immunotherapy response in patients with advanced non-small cell lung cancer.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Quality of Life Endpoints in Advanced Gastrointestinal Cancer Trials: Systematic Review of Inclusion, Challenges, and Implications (2020-2024).Clinical oncology (Royal College of Radiologists (Great Britain)) · 2026Pooled it
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Despite immune checkpoint inhibitors(ICIs) significantly improve clinical outcomes in patients with advanced non-small cell lung cancer (aNSCLC), disease progression is inevitable. A diverse patient-reported Quality-of-life(QoL) scales were used to predict outcomes for aNSCLC patients with atezolizumab using machine learning. Materials and Methods: This study analyzed the association between baseline QoL and clinical outcomes in aNSCLC patients with atezolizumab in 4 randomized clinical trials: the IMpower150 study (discovery cohort), the BIRCH, OAK and POPLAR study (validation cohorts). We identified quality of life subtypes (QoLS) by consensus clustering in the discovery cohort and predicted them in external validated cohorts. Results: We identified QoLS1 and QoLS2 via consensus clustering in the discovery cohort. Compared with QoLS1, QoLS2 was associated with significantly worse survival outcomes, including a shorter median overall survival (OS: 13.14 Conclusions: Our study demonstrated the potential of integrative machine learning in effectively analyzing baseline QoL and predicting clinical outcomes in aNSCLC patients undergoing atezolizumab immunotherapy.
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