Newman-Hung Nicole J, Shah Akash A, Kendal Joseph K, Bernthal Nicholas M, Wessel Lauren E
Department of Orthopaedic Surgery, University of California, Los Angeles, CA, USA.
, 15th Street, Suite 3140, Santa Monica, CA, 90404, USA.
J Orthop Surg Res. 2025 Aug 4;20(1):727. doi: 10.1186/s13018-025-06139-7.
Oncologic resection and endoprosthetic reconstruction of malignant bone tumors carries a high risk of complication and secondary surgery. Given the significant morbidity associated with reoperation in systemically compromised patients, accurate risk stratification is critical to patient counseling and shared decision-making. The purpose of this study was to develop a machine learning (ML) model for prediction of reoperation within one year of lower extremity tumor resection and endoprosthetic reconstruction.
Using data from the PARITY trial, 54 features across 604 lower extremity endoprosthetic reconstructions were evaluated as predictors of all-cause reoperation within one year. Logistic regression (LR), Random Forest, gradient boosting, AdaBoost, and XGBoost were used for model building. Standard metrics of area under receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), and Brier scores were used to evaluate model performance. Important features for the top-performing model were determined.
Of 604 lower extremity endoprosthetic reconstructions performed in the study period, 155 patients (25.7%) underwent at least one reoperation. The Gradient Boosting model had the highest discrimination (AUROC = 0.817, AUPRC = 0.690) of the tested models and was well-calibrated. Surgical site infection (SSI), operative time, white race, negative pressure wound therapy (NPWT) use, and female sex were the five most important features for model performance.
We report a well-calibrated ML-driven algorithm with high discriminatory power for the prediction of all-cause early reoperation following lower extremity tumor resection and endoprosthetic reconstruction. Preventing SSI remains paramount to avoiding the morbidity of reoperation after complex oncologic limb salvage surgeries.
恶性骨肿瘤的肿瘤切除及人工关节重建手术并发症及二次手术风险高。鉴于全身状况不佳的患者再次手术相关的显著发病率，准确的风险分层对于患者咨询及共同决策至关重要。本研究的目的是开发一种机器学习（ML）模型，用于预测下肢肿瘤切除及人工关节重建术后一年内的再次手术情况。
利用PARITY试验的数据，对604例下肢人工关节重建的54个特征进行评估，作为一年内全因再次手术的预测指标。采用逻辑回归（LR）、随机森林、梯度提升、AdaBoost和XGBoost进行模型构建。使用受试者工作特征曲线下面积（AUROC）、精确召回率曲线下面积（AUPRC）和布里尔评分等标准指标评估模型性能。确定表现最佳模型的重要特征。
在研究期间进行的604例下肢人工关节重建中，155例患者（25.7%）至少接受了一次再次手术。梯度提升模型在测试模型中具有最高的区分度（AUROC = 0.817，AUPRC = 0.690）且校准良好。手术部位感染（SSI）、手术时间、白种人、负压伤口治疗（NPWT）的使用和女性性别是模型性能的五个最重要特征。
我们报告了一种校准良好的、由ML驱动的算法，对下肢肿瘤切除及人工关节重建术后全因早期再次手术具有较高的区分能力。预防SSI对于避免复杂的肿瘤保肢手术后再次手术的发病率仍然至关重要。
