Wu Ji, Li Jian, Zhang Hao, Wu Luyang, Shen Xiping, Lv Wei
Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu Province, China; Department of Radiology, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, Jiangsu Province, China.
Department of Orthopedics, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Nantong University, Changshu, Jiangsu, China.
Eur J Radiol. 2025 Jan;182:111836. doi: 10.1016/j.ejrad.2024.111836. Epub 2024 Nov 14.
We aimed to develop and externally validate a tool for predicting short-term functional outcome after lumbar fusion surgery.
Data of 1520 patients underwent lumbar fusion from three institutions was analyzed. A total of 855 and 1251 radiomics features from paraspinal muscles were extracted from preoperative CT and MRI scans, respectively. Multivariable logistic regression was used to identify independent risk factors of poor functional status after surgery. We developed and externally validated a combined model by integrating radiomics score and clinical features. We evaluated the clinical utility and stability of the model using decision curve and calibration curve analysis. SHAP plot was used for interpretation of predictive results.
At multivariable analysis, radiomics score and 4 clinical features were identified as independent risk factors of poor functional outcome, and then a combined model was generated. This model had excellent performance, with AUCs of 0.85(95 %CI, 0.81-0.88), 0.82(95 %CI, 0.77-0.84), 0.79(95 %CI, 0.73-0.84) and 0.80(95 %CI, 0.76-0.83) in the derivation dataset and three independent test datasets, respectively. Moreover, this model showed great calibration and utility, outperforming the clinical model and radiomics score alone (both p < 0.05).
The combined model allows for accurate prediction of functional outcome after lumbar fusion surgery. The model could guide clinical decisions about the necessity of surgery for potential functional recovery.
我们旨在开发并外部验证一种用于预测腰椎融合手术后短期功能结局的工具。
分析了来自三个机构的1520例行腰椎融合手术患者的数据。分别从术前CT和MRI扫描中提取了总共855个和1251个椎旁肌的放射组学特征。采用多变量逻辑回归来确定术后功能状态不佳的独立危险因素。我们通过整合放射组学评分和临床特征开发并外部验证了一个联合模型。我们使用决策曲线和校准曲线分析评估了该模型的临床实用性和稳定性。使用SHAP图来解释预测结果。
在多变量分析中，放射组学评分和4个临床特征被确定为功能结局不佳的独立危险因素，然后生成了一个联合模型。该模型具有出色的性能，在推导数据集和三个独立测试数据集中的AUC分别为0.85（95%CI，0.81 - 0.88）、0.82（95%CI，0.77 - 0.84）、0.79（95%CI，0.73 - 0.84）和0.80（95%CI，0.76 - 0.83）。此外，该模型显示出良好的校准和实用性，优于单独的临床模型和放射组学评分（均p < 0.05）。
联合模型能够准确预测腰椎融合手术后的功能结局。该模型可为潜在功能恢复的手术必要性临床决策提供指导。
