Wang Jin, Cao Yuelong, Luo Xiaoqian, Zhuang Ruoyu, Wang Lijun, Cui Kaiying, Lu Tongxin, Hou Pengfei, Song Zhen, Wang Qing, Li Zhaoxin, Zhang Qiang, Hao Yanke
Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Shandong Public Health Clinical Center, Shandong University, Jinan, China.
Eur Spine J. 2025 May 17. doi: 10.1007/s00586-025-08905-x.
Differentiating between pyogenic (PS) and brucellar (BS) spondylitis is clinically challenging due to their similar clinical symptoms, with delayed diagnosis or misdiagnosis common, causing trouble for surgeons in selecting appropriate treatment strategies. Currently, radiology-based diagnostic models for PS and BS are lacking. This study aimed to combine magnetic resonance (MR) and radiographic imaging to elucidate the differences between PS and BS and develop a novel diagnostic model for differential diagnosis.
We collected and analyzed the differences between MR and radiological images of patients with PS and BS from two medical institutions. A nomogram was constructed using least absolute shrinkage and selection operator (LASSO) regression, alongside univariate and multivariate analyses to select the best features of the predictive model. Model discrimination, calibration, and clinical utility were assessed using receiver operating characteristic, calibration, and decision curve analyses.
Among the enrolled 342 patients with PS (n = 167) or BS (n = 175), we found significant differences in MR and radiological characteristics between the two groups. LASSO regression analysis revealed that thoracic involvement, involved vertebrae number, parrot beak osteophyte presence, endplate destruction, and intervertebral disc signal strength on T1-weighted sequences were independent predictive factors for differentiating between PS and BS. The imaging-based clinical prediction model showed high accuracy in the training and validation sets, with the area under the curve achieving 0.861 and 0.908, respectively, and a significant net benefit in the threshold probability, indicating high clinical potential of the model.
This imaging-based model offers a useful tool for efficiently differentiating PS and BS, facilitating prompt diagnosis and treatment and mitigating incorrect or delayed diagnosis.
由于化脓性脊柱炎（PS）和布鲁氏菌性脊柱炎（BS）临床症状相似，鉴别二者具有挑战性，延迟诊断或误诊常见，给外科医生选择合适的治疗策略带来困扰。目前，缺乏基于放射学的PS和BS诊断模型。本研究旨在结合磁共振（MR）和放射影像学来阐明PS和BS之间的差异，并开发一种用于鉴别诊断的新型诊断模型。
我们收集并分析了来自两家医疗机构的PS和BS患者的MR图像与放射学图像之间的差异。使用最小绝对收缩和选择算子（LASSO）回归构建列线图，并进行单变量和多变量分析以选择预测模型的最佳特征。使用受试者工作特征曲线、校准曲线和决策曲线分析评估模型的辨别力、校准度和临床实用性。
在纳入的342例PS患者（n = 167）或BS患者（n = 175）中，我们发现两组之间的MR和放射学特征存在显著差异。LASSO回归分析显示，胸椎受累、受累椎体数量、鹦鹉嘴样骨赘的存在、终板破坏以及T1加权序列上的椎间盘信号强度是鉴别PS和BS的独立预测因素。基于影像的临床预测模型在训练集和验证集中显示出高准确性，曲线下面积分别达到0.861和0.908，并且在阈值概率方面具有显著的净效益，表明该模型具有较高的临床潜力。
这种基于影像的模型为有效鉴别PS和BS提供了一种有用的工具，有助于及时诊断和治疗，并减少错误或延迟诊断。
