Background and objectives: Endoprosthetic reconstruction is the preferred approach for limb salvage surgery for many patients following malignant bone tumor resection. Implant failure is a common complication, however, there are no reliable means with which to offer patient-specific survival estimations. Implant survival predictions can set patient expectations and may guide treatment planning. This study aims to test and compare machine-learning models for the prediction of early tumor endoprosthetic implant survival.

Methods: A single-center retrospective series of 138 cases (mean age 41, 70 males, 68 females) was split into an 80:20 training and testing set. XGBoost, random forest, decision tree learning, and logistic regression were trained and assessed for model performance. After an initial review, age, sex, body mass index, diagnosis, location, resection length, and number of surgeries were selected as features. The output variables were 12-month, 24-month, and 36-month implant survival.

Results: Random forest had the best performance at 12, 24, and 36 months with an area under the curve (AUC) of 0.96, 0.89, 0.88; accuracy of 0.92, 0.83, 0.75; and Brier score of 0.09, 0.11, 0.20, respectively. Overall, the models performed better at 12 months compared to the other time points. The most important feature at 12 months was resection length (0.17), whereas age was most important at 24 months (0.15) and 36 months (0.17). Online tools were created based on the random forest models.

Conclusions: Machine learning models can be leveraged for the accurate prediction of early tumor endoprosthetic survival. These represent the first ML models used to predict endoprosthetic implant survival beyond 1 year and the first to include upper extremity implants. This offers better patient-specific prognostication which can help manage patient expectations and may guide recommendations.

Level of evidence: Level III.

Keywords: endoprosthesis; machine‐learning; megaprosthesis; random forest; tumor.

© 2025 Wiley Periodicals LLC.

背景和目标: 在恶性骨肿瘤切除后的保肢手术中，内植物重建是许多患者首选的治疗方法。然而，植入物失败是一个常见的并发症，目前还没有可靠的方法可以提供个性化的生存预测。植入物生存预测可以帮助设定患者的期望，并可能指导治疗计划。本研究旨在测试并比较用于预测早期肿瘤内植物生存率的机器学习模型。

方法: 该研究从单中心回顾性系列中选取了138例（平均年龄41岁，70名男性，68名女性）病例，并将其分为训练集和测试集（比例为80:20）。使用XGBoost、随机森林、决策树学习以及逻辑回归对模型性能进行训练及评估。经过初步审查后，选择了年龄、性别、体质指数、诊断结果、病变位置、切除长度以及手术次数作为特征变量。输出变量则为12个月、24个月和36个月的植入物生存率。

结果: 随机森林模型在12个月、24个月和36个月时表现出最佳性能，其曲线下面积（AUC）分别为0.96、0.89和0.88；准确率为0.92、0.83和0.75；Brier评分分别为0.09、0.11和0.20。总体来说，在12个月时，模型的性能优于其他时间点的表现。在12个月时最重要的特征是切除长度（0.17），而在24个月和36个月时年龄成为最重要因素（均为0.15和0.17）。基于随机森林模型创建了在线工具。

结论: 机器学习模型可以被用于准确预测早期肿瘤内植物的生存率。这是首次使用机器学习模型来预测超过一年的内植物植入物生存率，也是第一次包括上肢植体在内的研究。这为更好地进行个性化预后提供了可能，有助于管理患者的期望，并可能指导推荐。

证据水平: III级。

关键词: 内植物；机器学习；大型假体；随机森林；肿瘤。