In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI’s potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.
在脊柱肿瘤学领域，深度学习与计算机断层扫描（CT）成像技术的融合已展现出提升诊断准确性、优化治疗规划及改善患者预后的潜力。本系统性综述综合分析了人工智能（AI）在脊柱肿瘤CT成像中的应用证据。通过PRISMA指南进行文献检索，共纳入33项研究：其中12项（36.4%）聚焦脊柱恶性肿瘤检测，11项（33.3%）关注肿瘤分类，6项（18.2%）涉及预后预测，3项（9.1%）针对治疗规划，另有1项（3.0%）同时涵盖检测与分类。在分类研究中，7项（21.2%）采用机器学习区分良恶性病变，3项（9.1%）评估肿瘤分期或分级，2项（6.1%）运用影像组学进行生物标志物分类。预后研究包含3项（9.1%）预测病理性骨折等并发症，以及3项（9.1%）预测治疗结局。本文探讨了AI在提升工作流程效率、辅助临床决策和降低并发症风险方面的潜力，同时指出其在泛化性、可解释性及临床整合方面的局限性，并对AI在脊柱肿瘤学中的未来发展方向进行了展望。结论表明，尽管CT成像中的人工智能技术前景广阔，仍需进一步研究以验证其临床效能，并优化其在常规实践中的整合应用。
肿瘤(癌症)患者之家