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Abstract：
BACKGROUND: Despite being an effective treatment for osteonecrosis of the femoral head (ONFH), hip preservation surgery with fibula allograft (HPS&FA) still experiences numerous failures. Developing a prediction model based on clinical and radiomics predictors holds promise for addressing this issue.
METHODS: This study included 112 ONFH patients who underwent HPS&FA and were randomly divided into training and validation cohorts. Clinical data were collected, and clinically significant predictors were identified using univariate and multivariate analyses to develop a clinical prediction model (CPM). Simultaneously, the least absolute shrinkage and selection operator method was employed to select optimal radiomics features from preoperative hip computed tomography images, forming a radiomics prediction model (RPM). Furthermore, to enhance prediction accuracy, a clinical-radiomics prediction model (CRPM) was constructed by integrating all predictors. The predictive performance of the models was evaluated using receiver operating characteristic curve (ROC), area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis.
RESULTS: Age, Japanese Investigation Committee classification, postoperative use of glucocorticoids or alcohol, and non-weightbearing time were identified as clinical predictors. The AUC of the ROC curve for the CPM was 0.847 in the training cohort and 0.762 in the validation cohort. After incorporating radiomics features, the CRPM showed improved AUC values of 0.875 in the training cohort and 0.918 in the validation cohort. Decision curves demonstrated that the CRPM yielded greater medical benefit across most risk thresholds.
CONCLUSIONS: The CRPM serves as an efficient prediction model for assessing HPS&FA efficacy and holds potential as a personalized perioperative intervention tool to enhance HPS&FA success rates.
METHODS: This study included 112 ONFH patients who underwent HPS&FA and were randomly divided into training and validation cohorts. Clinical data were collected, and clinically significant predictors were identified using univariate and multivariate analyses to develop a clinical prediction model (CPM). Simultaneously, the least absolute shrinkage and selection operator method was employed to select optimal radiomics features from preoperative hip computed tomography images, forming a radiomics prediction model (RPM). Furthermore, to enhance prediction accuracy, a clinical-radiomics prediction model (CRPM) was constructed by integrating all predictors. The predictive performance of the models was evaluated using receiver operating characteristic curve (ROC), area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis.
RESULTS: Age, Japanese Investigation Committee classification, postoperative use of glucocorticoids or alcohol, and non-weightbearing time were identified as clinical predictors. The AUC of the ROC curve for the CPM was 0.847 in the training cohort and 0.762 in the validation cohort. After incorporating radiomics features, the CRPM showed improved AUC values of 0.875 in the training cohort and 0.918 in the validation cohort. Decision curves demonstrated that the CRPM yielded greater medical benefit across most risk thresholds.
CONCLUSIONS: The CRPM serves as an efficient prediction model for assessing HPS&FA efficacy and holds potential as a personalized perioperative intervention tool to enhance HPS&FA success rates.
摘要：
背景：尽管是股骨头坏死（ONFH）的有效治疗方法，腓骨同种异体移植（HPS和FA）的髋关节保留手术仍有许多失败。开发基于临床和放射组学预测因子的预测模型有望解决此问题。
方法：本研究包括112例接受HPS和FA的ONFH患者，随机分为训练和验证组。收集临床数据,使用单变量和多变量分析确定临床显著预测因子,以建立临床预测模型(CPM).同时,采用最小绝对收缩和选择算子方法从术前髋关节计算机断层扫描图像中选择最佳的影像组学特征，形成影像组学预测模型(RPM)。此外，为了提高预测精度，通过整合所有预测因子构建临床-影像组学预测模型(CRPM).使用受试者工作特征曲线(ROC)评估模型的预测性能，曲线下面积（AUC），DeLong测试,校正曲线,和决策曲线分析。
结果：年龄，日本调查委员会分类，术后使用糖皮质激素或酒精，和非负重时间被确定为临床预测因子。CPM的ROC曲线的AUC在训练队列中为0.847,在验证队列中为0.762。整合影像组学功能后，CRPM显示训练队列的AUC值改善,为0.875,验证队列为0.918.决策曲线表明，在大多数风险阈值中，CRPM产生了更大的医疗效益。
结论：CRPM可作为评估HPS和FA疗效的有效预测模型，并具有作为提高HPS和FA成功率的个性化围手术期干预工具的潜力。
方法：本研究包括112例接受HPS和FA的ONFH患者，随机分为训练和验证组。收集临床数据,使用单变量和多变量分析确定临床显著预测因子,以建立临床预测模型(CPM).同时,采用最小绝对收缩和选择算子方法从术前髋关节计算机断层扫描图像中选择最佳的影像组学特征，形成影像组学预测模型(RPM)。此外，为了提高预测精度，通过整合所有预测因子构建临床-影像组学预测模型(CRPM).使用受试者工作特征曲线(ROC)评估模型的预测性能，曲线下面积（AUC），DeLong测试,校正曲线,和决策曲线分析。
结果：年龄，日本调查委员会分类，术后使用糖皮质激素或酒精，和非负重时间被确定为临床预测因子。CPM的ROC曲线的AUC在训练队列中为0.847,在验证队列中为0.762。整合影像组学功能后，CRPM显示训练队列的AUC值改善,为0.875,验证队列为0.918.决策曲线表明，在大多数风险阈值中，CRPM产生了更大的医疗效益。
结论：CRPM可作为评估HPS和FA疗效的有效预测模型，并具有作为提高HPS和FA成功率的个性化围手术期干预工具的潜力。
