Wilson Seth B, Ward Jacob, Munjal Vikas, Lam Chi Shing Adrian, Patel Mayur, Zhang Ping, Xu David S, Chakravarthy Vikram B
Department of Neurosurgery, The Ohio State University, Columbus, OH, USA.
Department of Computer Science and Engineering, The Ohio State University College of Engineering, Columbus, OH, USA.
Global Spine J. 2025 Jan;15(1):210-227. doi: 10.1177/21925682241261342. Epub 2024 Jun 11.
Narrative Review.
Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology.
This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies.
Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors.
Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.
叙述性综述。
机器学习（ML）是医学和外科领域人工智能的最新进展之一，有可能显著影响医生诊断、预测和治疗脊柱肿瘤的方式。在脊柱肿瘤学领域，ML被用于分析和解读医学影像，并以惊人的准确性对肿瘤进行分类。作者进行了一项叙述性综述，专门探讨机器学习在脊柱肿瘤学中的应用。
本研究按照系统评价和Meta分析的首选报告项目（PRISMA）方法进行。对PubMed、EMBASE、科学网、Scopus和Cochrane图书馆数据库自创建以来的文献进行系统综述，以呈现所有使用搜索词“[[机器学习]或[人工智能]]与[[脊柱肿瘤学]或[脊柱癌]]”的临床研究。数据包括提取的研究，包括算法、训练和测试规模、报告的结果。根据使用机器学习算法研究的肿瘤类型，将研究分为原发性、转移性、两者皆有和硬膜内肿瘤。至少两名独立评审员对研究进行评估、数据提取和质量评估。
从初步搜索结果筛选的480篇参考文献中，有45项研究符合纳入标准。研究按转移性、原发性和硬膜内肿瘤分组。与脊柱肿瘤学相关的大多数ML研究集中于利用临床和影像特征的组合对死亡率和虚弱程度进行风险分层。总体而言，这些研究表明，ML在肿瘤检测、鉴别、分割、预测生存、预测原发性、转移性或硬膜内脊柱肿瘤患者的再入院率方面是一个有用的工具。
专门的神经网络和深度学习算法已证明在预测恶性概率和辅助诊断方面非常有效。ML算法可以根据影像和临床特征预测肿瘤复发或进展的风险。此外，ML可以优化治疗计划，如预测肿瘤和周围正常组织的放射治疗剂量分布或手术切除计划。它有可能显著提高医疗服务的准确性和效率，从而改善患者的治疗效果。
