Sanker Vivek, Dawer Prachi, Thaller Alexander, Li Zhikai, Heesen Philip, Hariharan Srinath, Nordin Emil O R, Cavagnaro Maria Jose, Ratliff John, Desai Atman
Department of Neurosurgery, Stanford University, Palo Alto, CA 94305, USA.
Department of Neurosurgery, University College of Medical Sciences, New Delhi 110095, India.
J Clin Med. 2025 Aug 20;14(16):5885. doi: 10.3390/jcm14165885.
Spinal metastases can cause significant impairment of neurological function and quality of life. Hence, personalized clinical decision-making based on prognosis and likely outcome is desirable. The effectiveness of AI in predicting complications and treatment outcomes for patients with spinal metastases is assessed. A thorough search was carried out through the PubMed, Scopus, Web of Science, Embase, and Cochrane databases up until 27 January 2025. Included were studies that used AI-based models to predict outcomes for adult patients with spinal metastases. Three reviewers independently extracted the data, and screening was conducted in accordance with PRISMA principles. AUC results were pooled using a random-effects model, and the PROBAST program was used to evaluate the study's quality. Included were 47 articles totaling 25,790 patients. For training, internal validation, and external validation, the weighted average AUCs were 0.762, 0.876, and 0.810, respectively. The Skeletal Oncology Research Group machine learning algorithms (SORG-MLAs) were the ones externally validated the most, continuously producing AUCs > 0.84 for 90-day and 1-year mortality. Models based on radiomics showed promise in preoperative planning, especially for outcomes of radiation and concealed blood loss. Most research concentrated on breast, lung, and prostate malignancies, which limited its applicability to less common tumors. AI models have shown reasonable accuracy in predicting mortality, ambulatory status, blood loss, and surgical complications in patients with spinal metastases. Wider implementation necessitates additional validation, data standardization, and ethical and regulatory framework evaluation. Future work should concentrate on creating multimodal, hybrid models and assessing their practical applications.
脊柱转移瘤可导致神经功能和生活质量严重受损。因此，基于预后和可能结果进行个性化临床决策是可取的。评估了人工智能在预测脊柱转移瘤患者并发症和治疗结果方面的有效性。截至2025年1月27日，通过PubMed、Scopus、Web of Science、Embase和Cochrane数据库进行了全面检索。纳入的研究使用基于人工智能的模型来预测成年脊柱转移瘤患者的结果。三名评审员独立提取数据，并按照PRISMA原则进行筛选。使用随机效应模型汇总AUC结果，并使用PROBAST程序评估研究质量。纳入47篇文章，共25790名患者。对于训练、内部验证和外部验证，加权平均AUC分别为0.762、0.876和0.810。骨骼肿瘤学研究组机器学习算法（SORG-MLAs）是外部验证最多的算法，对于90天和1年死亡率，其AUC持续大于0.84。基于放射组学的模型在术前规划中显示出前景，尤其是对于放疗结果和隐匿性失血。大多数研究集中在乳腺癌、肺癌和前列腺癌，这限制了其对不太常见肿瘤的适用性。人工智能模型在预测脊柱转移瘤患者的死亡率、活动状态、失血和手术并发症方面显示出合理的准确性。更广泛的应用需要额外的验证、数据标准化以及伦理和监管框架评估。未来的工作应集中在创建多模式、混合模型并评估其实际应用。
