The Skeletal Oncology Research Group machine-learning algorithms (SORG-MLAs) estimate 90-day and 1-year survival in patients with long-bone metastases undergoing surgical treatment and have demonstrated good discriminatory ability on internal validation. However, the performance of a prediction model could potentially vary by race or region, and the SORG-MLA must be externally validated in an Asian cohort. Furthermore, the authors of the original developmental study did not consider the Eastern Cooperative Oncology Group (ECOG) performance status, a survival prognosticator repeatedly validated in other studies, in their algorithms because of missing data.

中文翻译：

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SORG 机器学习算法用于预测接受手术治疗的四肢转移患者生存率的国际验证。

骨骼肿瘤学研究小组机器学习算法 (SORG-MLA) 估计接受手术治疗的长骨转移患者的 90 天和 1 年生存率，并在内部验证中表现出良好的区分能力。然而，预测模型的性能可能会因种族或地区而异，并且 SORG-MLA 必须在亚洲队列中进行外部验证。此外，最初的发展研究的作者由于缺少数据，在他们的算法中没有考虑东部肿瘤合作组（ECOG）的表现状态，这是在其他研究中反复验证的生存预测指标。