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
Abstract Background Preference-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 algor...
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
|
| Series: | Health and Quality of Life Outcomes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12955-025-02394-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849767293796483072 |
|---|---|
| author | Longlin Jiang Kexun Li Simiao Lu Zhou Hong Yifang Wang Qin Xie Qin He Sirui Wei Aoru Zhou Hong Kang Xuefeng Leng Qing Yang Yan Miao |
| author_facet | Longlin Jiang Kexun Li Simiao Lu Zhou Hong Yifang Wang Qin Xie Qin He Sirui Wei Aoru Zhou Hong Kang Xuefeng Leng Qing Yang Yan Miao |
| author_sort | Longlin Jiang |
| collection | DOAJ |
| description | Abstract Background Preference-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). Method The 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 2 , root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to screen the optimal model. Results The 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 2 = 0.753, RMSE = 0.074, MAE = 0.057, MAPE = 8.169%. Conclusions This 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. |
| format | Article |
| id | doaj-art-929a737295b94c2aad8dff6263d1d58a |
| institution | DOAJ |
| issn | 1477-7525 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Health and Quality of Life Outcomes |
| spelling | doaj-art-929a737295b94c2aad8dff6263d1d58a2025-08-20T03:04:15ZengBMCHealth and Quality of Life Outcomes1477-75252025-07-0123111210.1186/s12955-025-02394-8Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methodsLonglin Jiang0Kexun Li1Simiao Lu2Zhou Hong3Yifang Wang4Qin Xie5Qin He6Sirui Wei7Aoru Zhou8Hong Kang9Xuefeng Leng10Qing Yang11Yan Miao12Department of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaNursing department, Sichuan Clinical Research Center for Cancer. Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of ChinaAbstract Background Preference-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). Method The 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 2 , root mean square error (RMSE),mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to screen the optimal model. Results The 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 2 = 0.753, RMSE = 0.074, MAE = 0.057, MAPE = 8.169%. Conclusions This 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.https://doi.org/10.1186/s12955-025-02394-8Lung CancerQuality of lifeQLQ-C30QLQ-LC13MappingSF-6D |
| spellingShingle | Longlin Jiang Kexun Li Simiao Lu Zhou Hong Yifang Wang Qin Xie Qin He Sirui Wei Aoru Zhou Hong Kang Xuefeng Leng Qing Yang Yan Miao 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 Health and Quality of Life Outcomes Lung Cancer Quality of life QLQ-C30 QLQ-LC13 Mapping SF-6D |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Lung Cancer Quality of life QLQ-C30 QLQ-LC13 Mapping SF-6D |
| url | https://doi.org/10.1186/s12955-025-02394-8 |
| work_keys_str_mv | AT longlinjiang mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT kexunli mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT simiaolu mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT zhouhong mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT yifangwang mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT qinxie mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT qinhe mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT siruiwei mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT aoruzhou mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT hongkang mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT xuefengleng mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT qingyang mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods AT yanmiao mappingtheeortcqlqc30andqlqlc13tothesf6dutilityindexinpatientswithlungcancerusingmachinelearningandtraditionalregressionmethods |