Background:The Spinal Instability Neoplastic Score (SINS) guides treatment for patients with spinal tumors, but issues arise with complexity, interobserver variability, and time demands. Large language models (LLMs) may help overcome these limitations.Objectives:This study evaluates the accuracy and efficiency of a privacy-preserving LLM (PP-LLM) for SINS calculation, with and without clinician involvement, to assess its feasibility as a clinical decision-support tool.Methods: This retrospective observational study was granted a Domain-Specific Review Board waiver owing to minimal risk. Patients from 2020 to 2022 were included. A PP-LLM was employed to maintain secure handling of patient data. A consensus SINS reference standard was established by musculoskeletal radiologists and an orthopedic surgeon. Eight orthopedic and oncology trainees were divided into two groups to calculate SINS, with and without PP-LLM assistance. LLM-predicted scores were also generated independently of any human input.Results:The main outcomes were agreement with the reference standard (measured by intraclass correlation coefficients [ICCs]) and time required for SINS calculation. The LLM-assisted method achieved excellent agreement (ICC = 0.993, 95%CI = 0.991–0.994), closely followed by the LLM-predicted approach (ICC = 0.990, 95%CI = 0.984–0.993). Clinicians working without LLM support showed a significantly lower ICC compared to both LLM methods (0.968, 95%CI = 0.960–0.975) (bothp< 0.001). The LLM alone produced scores in approximately 5 s, while the median scoring time for LLM-assisted clinicians was 60.0 s (IQR = 46.0–80.0), notably shorter than the 83.0 s (IQR = 58.0–124.0) required without LLM assistance.Conclusions:An LLM-based approach, whether used autonomously or in conjunction with clinical expertise, enhances both accuracy and efficiency in SINS calculation. Adopting this technology may streamline oncologic workflows and facilitate more timely interventions for patients with spinal metastases.
背景：脊柱肿瘤不稳定性评分（SINS）用于指导脊柱肿瘤患者的治疗，但其复杂性、观察者间差异及耗时问题限制了临床应用。大型语言模型（LLMs）可能有助于克服这些局限性。 目的：本研究评估一种隐私保护型大型语言模型（PP-LLM）在有无临床医生参与情况下计算SINS的准确性与效率，探讨其作为临床决策支持工具的可行性。 方法：这项回顾性观察研究因风险极低获得特定领域审查委员会豁免。纳入2020年至2022年患者数据。采用PP-LLM确保患者数据安全处理。由肌肉骨骼放射科医生和骨科医生共同建立SINS共识参考标准。8名骨科与肿瘤科培训医师分为两组，分别在有无PP-LLM辅助下计算SINS评分。同时独立生成完全由LLM预测的评分。 结果：主要结局指标包括与参考标准的一致性（通过组内相关系数[ICC]衡量）及SINS计算耗时。LLM辅助方法达到极佳一致性（ICC=0.993，95%CI=0.991–0.994），完全由LLM预测的方法紧随其后（ICC=0.990，95%CI=0.984–0.993）。无LLM辅助的临床医生ICC值显著低于两种LLM方法（0.968，95%CI=0.960–0.975）（两者p<0.001）。单独使用LLM生成评分仅需约5秒，LLM辅助临床医生的中位评分时间为60.0秒（IQR=46.0–80.0），显著短于无辅助时的83.0秒（IQR=58.0–124.0）。 结论：基于LLM的方法，无论是独立应用还是结合临床专业知识，均能提升SINS计算的准确性与效率。采用该技术可优化肿瘤诊疗流程，为脊柱转移瘤患者提供更及时的干预。
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