ArticleHealth and quality of life outcomes2025
Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.
Article in Health and quality of life outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The impact of traditional mind-body exercises on pulmonary function, exercise capacity, and quality of life in patients with lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
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- Development and validation of a nomogram for predicting heterogeneous patient-reported outcome recovery trajectories after lung cancer surgery: a retrospective study.Journal of cardiothoracic surgery · 2026Article
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Authors and funding
13 authors.
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Abstract
backgroundPreference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health economic evaluations. However, these measures are rarely included in clinical trials for lung cancer. This study aims to develop mapping algorithms to predict SF-6D health utility scores from the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core (EORTC QLQ-C30) and Quality of Life Questionnaire-Lung Cancer 13 (QLQ-LC13).
methodThe study sample comprised a Chinese population with lung cancer (n = 625). Traditional regression techniques, including Ordinary Least Squares regression, Generalized Linear Model, as well as machine learning techniques, such as Gradient Boosting Tree, Support Vector Regression, Ridge Regression are used. Five-fold cross-validation was performed. The performance metrics used to evaluate the models including R
resultsThe mean and median of SF-6D health utility values were 0.774 (SD = 0.154) and 7.795, respectively. The model with the best mapping performance was the Ridge regression model Five-fold cross-validation (CV) results show that the Ridge regression model has the best mapping performance, the final prediction indexes are R
conclusionsThis study developed an optimized mapping algorithm to predict the utility index from the QLQ-C30 QLQ-LC13 to the SF-6D. This algorithm offers provides an effective alternative for estimating SF-6D estimation when the preference-based health utility values are unavailable.
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