Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models
Abstract To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of...
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Nature Portfolio
2024-10-01
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| Online Access: | https://doi.org/10.1038/s41598-024-74418-0 |
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| author | Hongyang Jiang Aihui Liu Zhenhua Ying |
| author_facet | Hongyang Jiang Aihui Liu Zhenhua Ying |
| author_sort | Hongyang Jiang |
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| description | Abstract To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of 54 patients with CP and 71 healthy controls was randomly separated into training and validation groups, using a 7:3 ratio. Radiomics features were extracted from grey-matter and white-matter in the filtered mwp0* image. The Mann-Whitney U test, Spearman’s rank correlation test, and least absolute shrinkage and selection operator (LASSO) were utilized to select features. An XGBoost model was created based on these features, and Shapley Additive exPlanations (SHAP) was used for personalization and visual interpretation. A nomogram was developed for the diagnosis of FMS subgroups, utilizing radiomics scores and clinical predictors. The efficacy of the nomogram was evaluated using the area under the receiver operating characteristic curve, while decision curve analysis was employed to evaluate its clinical efficacy. The XGBoost model displays stability in the training validation group, indicating lower overfitting of CP model. The nomogram model combined with the rad-score has a greater ability to distinguish between typical and sub-clinical than the clinical factor model alone. We developed and validated a CP diagnosis model by XGBoost and realized model visualization through SHAP. The rad-score obtained by machine learning was used to build a nomogram model that combines clinical scales to distinguish patients with typical and sub-clinical fibromyalgia. |
| format | Article |
| id | doaj-art-6ec6b9cdf5bd44909ad3fdb07c40414f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-6ec6b9cdf5bd44909ad3fdb07c40414f2025-08-20T03:10:06ZengNature PortfolioScientific Reports2045-23222024-10-0114111010.1038/s41598-024-74418-0Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning modelsHongyang Jiang0Aihui Liu1Zhenhua Ying2Medical College of Soochow UniversityCenter for General Practice Medicine, Department of Rheumatology and Immunology, Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital), Hangzhou Medical CollegeMedical College of Soochow UniversityAbstract To provide objective diagnostic markers for fibromyalgia symptoms (FMS) diagnosis, we have created interpretable extreme gradient boosting (XGBoost) models using radiomics to aid in the diagnosis of chronic pain (CP) and to develop nomogram models for diagnosing subgroups of FMS. A group of 54 patients with CP and 71 healthy controls was randomly separated into training and validation groups, using a 7:3 ratio. Radiomics features were extracted from grey-matter and white-matter in the filtered mwp0* image. The Mann-Whitney U test, Spearman’s rank correlation test, and least absolute shrinkage and selection operator (LASSO) were utilized to select features. An XGBoost model was created based on these features, and Shapley Additive exPlanations (SHAP) was used for personalization and visual interpretation. A nomogram was developed for the diagnosis of FMS subgroups, utilizing radiomics scores and clinical predictors. The efficacy of the nomogram was evaluated using the area under the receiver operating characteristic curve, while decision curve analysis was employed to evaluate its clinical efficacy. The XGBoost model displays stability in the training validation group, indicating lower overfitting of CP model. The nomogram model combined with the rad-score has a greater ability to distinguish between typical and sub-clinical than the clinical factor model alone. We developed and validated a CP diagnosis model by XGBoost and realized model visualization through SHAP. The rad-score obtained by machine learning was used to build a nomogram model that combines clinical scales to distinguish patients with typical and sub-clinical fibromyalgia.https://doi.org/10.1038/s41598-024-74418-0FibromyalgiaXGBoostRadiomicsChronic painMRI |
| spellingShingle | Hongyang Jiang Aihui Liu Zhenhua Ying Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models Scientific Reports Fibromyalgia XGBoost Radiomics Chronic pain MRI |
| title | Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models |
| title_full | Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models |
| title_fullStr | Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models |
| title_full_unstemmed | Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models |
| title_short | Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models |
| title_sort | identification of texture mri brain abnormalities on fibromyalgia syndrome using interpretable machine learning models |
| topic | Fibromyalgia XGBoost Radiomics Chronic pain MRI |
| url | https://doi.org/10.1038/s41598-024-74418-0 |
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