Sanker Vivek, Gowda Poorvikha, Thaller Alexander, Li Zhikai, Heesen Philip, Qiang Zekai, 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, St John's Medical College, Bangalore 560034, Karnataka, India.
J Clin Med. 2025 Aug 20;14(16):5877. doi: 10.3390/jcm14165877.
Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like 'spine metastases', 'artificial intelligence', 'machine learning', 'deep learning', and 'diagnosis'. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact.
脊柱转移是继肺和肝之后第三常见的转移部位。通过CT、MRI、PET和骨闪烁显像等成像方式进行人工检测成本高且效率低。初步的人工智能（AI）技术和计算机辅助检测（CAD）系统已尝试改善肿瘤成像中的病变检测、分割和治疗反应评估。本综述的目的是评估AI在多模态成像技术中诊断脊柱转移的当前应用情况。使用“脊柱转移”“人工智能”“机器学习”“深度学习”和“诊断”等特定关键词对PubMed、Scopus、Web of Science Advance、Cochrane和Embase（Ovid）等数据库进行了检索。研究筛选遵循PRISMA指南。从每篇纳入文章中提取相关变量，如原发肿瘤类型、队列规模和预测模型性能指标：受试者操作特征曲线下面积（AUC）、准确性、敏感性、特异性、内部验证和外部验证。采用随机效应荟萃分析模型来考虑研究之间的变异性。使用PROBAST工具进行质量评估。本综述纳入了2007年至2024年发表的39项研究，共涉及6267例患者。三种最常见的原发肿瘤是肺癌（56.4%）、乳腺癌（51.3%）和前列腺癌（41.0%）。四项研究报告了模型训练的AUC值，16项报告了内部验证的AUC值，五项报告了外部验证的AUC值。加权平均AUC分别为0.971（训练）、0.947（内部验证）和0.819（外部验证）。偏倚风险在分析领域最高，22项研究（56%）被评为高风险，主要原因是外部验证不足和过度拟合。基于AI的方法有望增强对多种成像模态下脊柱转移瘤病变的检测、分割和特征描述。未来的研究应专注于通过更大、更多样化的训练数据集开发更具通用性的模型，整合临床和成像数据，并进行前瞻性验证研究以证明有意义的临床影响。
