Medical College of Soochow University, Suzhou, China.
Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Sci Rep. 2024 Oct 9;14(1):23525. doi: 10.1038/s41598-024-74418-0.
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.
为了提供纤维肌痛症状（FMS）诊断的客观诊断标志物，我们使用放射组学创建了可解释的极端梯度提升（XGBoost）模型，以辅助慢性疼痛（CP）的诊断，并为 FMS 的亚组开发列线图模型。一组 54 名 CP 患者和 71 名健康对照者被随机分为训练组和验证组，比例为 7:3。从滤波后的 mwp0*图像的灰质和白质中提取放射组学特征。使用 Mann-Whitney U 检验、Spearman 秩相关检验和最小绝对值收缩和选择算子（LASSO）选择特征。基于这些特征创建了 XGBoost 模型，并使用 Shapley Additive exPlanations（SHAP）进行个性化和可视化解释。利用放射组学评分和临床预测因子，为 FMS 亚组开发了列线图。使用受试者工作特征曲线下面积评估列线图的疗效，同时使用决策曲线分析评估其临床疗效。XGBoost 模型在训练验证组中表现出稳定性，表明 CP 模型的过拟合程度较低。列线图模型与 rad-score 结合具有比仅基于临床因素模型更好的区分典型和亚临床患者的能力。我们通过 XGBoost 开发并验证了 CP 诊断模型，并通过 SHAP 实现了模型可视化。通过机器学习获得的 rad-score 用于构建一个列线图模型，该模型结合了临床量表，以区分典型和亚临床纤维肌痛患者。
