Jiang Hongyang, Liu Aihui, Cao Yihan, Lin Zhimin, Jiang Haixu, Liu Shengyan, Peng Qiuwei, Wu Xia, Liu Yuchen, Yu Xinbo, Wei Maming, Pan Yalin, Li Chen, Ying Zhenhua
Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Center for General Practice Medicine, Department of Rheumatology and Immunology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Sci Rep. 2025 May 28;15(1):18651. doi: 10.1038/s41598-025-99690-6.
SAPHO syndrome is an inflammatory disorder with bone and cutaneous manifestations, for which whole-body bone scintigraphy (WBBS) is frequently used in diagnosis. The WBBS findings of SAPHO syndromes and secondary bone tumors (SBT) have overlapping features, posing diagnostic challenges. In this multicenter study, we aim to identify different bone and joint involvement patterns between the two disease entities through multiple methods to build machine-learning models and explore interpretable variables. The study included 1,193 patients, of which 593 were diagnosed with SAPHO syndrome and 600 with SBT. LASSO regression, logistic regression, and random forest techniques were applied in the training set to identify significant risk factors. Manual management and other methods were evaluated in the validation set to identify the top-performing model and the most interpretable terms. The study developed a model using 15 manually selected terms and multiple machine learning techniques, which demonstrated high diagnostic accuracy in the G1 dataset for (training AUC 0.934, testing AUC 0.929, accuracy = 88.3%, precision = 88.7%, Recall = 88.3%, F1 score = 0.882). The model was compared with logistic regression and random forest models and showed consistent results in the G2 dataset for external validation (AUC 0.957, Youden index = 0.806, sensitivity = 0.820, specificity = 0.986). The pelvis, femur, and ribs (excluding anterior ribs 1st-5th) and thoracic vertebrae 1st-8th were significant predictors of SBT, whereas the sacroiliac joints, sternum, foot, anterior ribs 1st-5th, and clavicle were indicative of SAPHO. This study assesses the effectiveness of WBBS terms in identifying SBT from SAPHO syndrome and utilizes machine learning to help screen features for patients. The final model demonstrates its dependability, providing a valuable tool for accurate and timely diagnosis.
滑膜炎伴痤疮、脓疱疮、骨肥厚和骨炎综合征（SAPHO综合征）是一种具有骨骼和皮肤表现的炎症性疾病，全身骨闪烁显像（WBBS）常用于其诊断。SAPHO综合征和继发性骨肿瘤（SBT）的WBBS表现具有重叠特征，给诊断带来挑战。在这项多中心研究中，我们旨在通过多种方法识别这两种疾病实体之间不同的骨骼和关节受累模式，以建立机器学习模型并探索可解释变量。该研究纳入了1193例患者，其中593例被诊断为SAPHO综合征，600例被诊断为SBT。在训练集中应用套索回归、逻辑回归和随机森林技术来识别显著危险因素。在验证集中评估人工管理和其他方法，以确定表现最佳的模型和最具可解释性的术语。该研究使用15个手动选择的术语和多种机器学习技术开发了一个模型，该模型在G1数据集中显示出较高的诊断准确性（训练集AUC为0.934，测试集AUC为0.929，准确率 = 88.3%，精确率 = 88.7%，召回率 = 88.3%，F1分数 = 0.882）。将该模型与逻辑回归模型和随机森林模型进行比较，在用于外部验证的G2数据集中显示出一致的结果（AUC为0.957，约登指数 = 0.806，灵敏度 = 0.820，特异度 = 0.986）。骨盆、股骨和肋骨（不包括第1 - 5肋前肋）以及第1 - 8胸椎是SBT的显著预测因素，而骶髂关节、胸骨、足部、第1 - 5肋前肋和锁骨则提示为SAPHO综合征。本研究评估了WBBS术语在从SAPHO综合征中识别SBT方面的有效性，并利用机器学习帮助筛选患者的特征。最终模型证明了其可靠性，为准确及时的诊断提供了有价值的工具。
