Prediction of intraoperative press-fit stability of the acetabular cup in total hip arthroplasty using radiomics-based machine learning models

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2024-09-19 DOI:10.1016/j.ejrad.2024.111751

Bin He , Xin Zhang , Shengwang Peng , Dong Zeng , Haicong Chen , Zhenming Liang , Huan Zhong , Hanbin Ouyang

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引用次数: 0

Abstract

Background

Preoperative prediction of the acetabular cup press-fit stability in total hip arthroplasty is necessary for clinical decision-making. This study aims to establish and validate machine learning models to investigate the feasibility of predicting the intraoperative press-fit stability of the acetabular cup in total hip arthroplasty (THA).

Methods

226 patients who underwent primary THA from 2018 to 2022 in our hospital were retrospectively enrolled. Patients were divided into press-fit stable or unstable groups according to the intraoperative pull-out test of the implanted cup. Then, they were randomly assigned to the training or test cohort in an 8:2 ratio. We used 3Dslicer software to segment the region of interest (ROI) of the patient’s bilateral hip X-ray to extract radiomics features. The least absolute shrinkage and selection operator (LASSO) regression was used in our feature selection. Finally, four machine learning models were employed in this study, including support vector machine (SVM), random forest (RF), logistic regression (LR), and XGBoost (XGB). Decision curve analysis (DCA), and receiver operating characteristic (ROC) curves of the models were plotted. The area under the curve (AUC), diagnostic accuracy, sensitivity, and specificity were calculated as well. The AUCs of the four models were compared using the DeLong test.

Results

Twenty-seven valuable radiomics features were determined by dimensionality reduction and selection. Regarding to the DeLong test, the AUC of the XGB model was significantly different from those of the other three models. (p < 0.05). Among all models, the XGB model exhibited the best performance with an AUC of 0.823 (95 % CI: 0.711–0.919) in the test cohort and showed optimal clinical efficacy according to the DCA.

Conclusion

Machine learning models based on X-ray radiomics can accurately predict the intraoperative press-fit stability of implanted cups preoperatively, providing surgeons with valuable information to lower the complication risk in THA.

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利用基于放射组学的机器学习模型预测全髋关节置换术中髋臼杯的术中压配稳定性

背景术前预测全髋关节置换术中髋臼杯的压合稳定性对临床决策非常必要。本研究旨在建立和验证机器学习模型，以研究预测全髋关节置换术（THA）中髋臼杯术中压配稳定性的可行性。方法回顾性纳入2018年至2022年在我院接受初次THA的226例患者。根据植入髋臼杯的术中拉出试验，将患者分为压配稳定组和不稳定组。然后，按照 8:2 的比例将他们随机分配到训练组或测试组。我们使用 3Dslicer 软件分割患者双侧髋关节 X 光片的感兴趣区（ROI），提取放射组学特征。在选择特征时，我们使用了最小绝对收缩和选择算子（LASSO）回归法。最后，本研究采用了四种机器学习模型，包括支持向量机（SVM）、随机森林（RF）、逻辑回归（LR）和 XGBoost（XGB）。绘制了各模型的决策曲线分析（DCA）和接收者操作特征曲线（ROC）。同时还计算了曲线下面积（AUC）、诊断准确性、灵敏度和特异性。结果通过降维和选择确定了 27 个有价值的放射组学特征。根据 DeLong 检验，XGB 模型的 AUC 与其他三个模型有显著差异。(P<；0.05）。结论基于 X 射线放射组学的机器学习模型可以在术前准确预测植入杯的术中压合稳定性，为外科医生降低 THA 并发症风险提供有价值的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.