Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.

中文翻译：

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两种现代生存预测工具 SORG-MLA 和 METSSS 在接受手术后放疗和单独放疗的局部治疗的症状性长骨转移患者中的比较。


理想情况下，应在确定某种类型的局部治疗之前对有症状的骨骼转移患者进行生存估计。然而，目前可用的生存预测工具是使用手术治疗或单独接受局部放疗的患者的数据生成的，这引发了人们对它们是否能很好地推广到所有接受评估的患者的担忧。骨骼肿瘤学研究小组机器学习算法 （SORG-MLA），使用手术治疗患者的基于机构的数据进行训练，以及转移位置、老年人、肿瘤原发性、性别、疾病/合并症和放疗部位模型 （METSSS），使用单独接受放疗治疗的患者的基于登记的数据进行训练，是最近开发的两种生存预测模型，但它们尚未在尚未制定局部治疗策略的患者身上进行测试决定。