用大模型过滤报告，从35万张CT中提取健康参考数据
Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering
- 五模型交叉验证，精准识别影像报告中的病灶线索
- 构建106个解剖结构的全身体积与密度参考图谱
- 适合做临床量化表型分析、多中心研究和筛查评估
定量CT生物标志物（如器官体积和组织衰减）的解读需要大规模健康参考数据。但临床数据常严重混杂病理信息。本文开发了一种基于证据的大型语言模型集成系统，通过交叉验证过滤超过35万次常规CT检查的放射科报告中的病理发现，构建去病理化队列。五个大模型首先基于报告原文标记结构级异常候选，再通过交叉验证解决分歧。利用分布感知的广义加性模型（location, scale, and shape），建立覆盖成人期106个解剖结构（体积与衰减）的全身体参考图谱，考虑年龄、性别、增强方式及扫描参数。纵向分析揭示结构与对比剂依赖性的变化模式，不同于横断面趋势。该资源支持基于常规CT的协变量调整百分位评分，推动标准化定量表型分析、多中心影像研究及可扩展的机遇性筛查。
原文摘要 · Abstract (English)
Interpreting quantitative CT biomarkers, such as organ volume and tissue attenuation, requires large-scale healthy reference distributions. However, creating these is challenging because clinical datasets are often heavily enriched with pathology. Here, we develop an evidence-grounded, cross-verified large language model (LLM) ensemble to filter pathological findings from radiology reports, enabling the construction of pathology-reduced cohorts from over 350,000 CT examinations. Five LLMs, first, flag structure-level abnormality candidates grounded in verbatim report evidence and, second, resolve disagreements via cross-verification. Using distribution-aware generalized additive models for location, scale, and shape, we establish comprehensive whole-body reference charts for 106 anatomical structures (volumes and attenuation) across adulthood, accounting for age, sex, contrast enhancement, and acquisition parameters. Longitudinal analyses reveal structure- and contrast-dependent changes distinct from cross-sectional trends. These resources facilitate covariate-adjusted centile scoring from routine CT, supporting standardized quantitative phenotyping, multi-site imaging studies, and scalable opportunistic screening research.
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