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Updated: Jun 9, 2026

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

利用大型语言模型提取尤文肉瘤的预后病理特征

Jingwei Huang, Ayesha Batool, Zifan Gu

bioRxiv : the preprint server for biology

|February 27, 2026

概括

大型语言模型 (LLM) 准确地从Ewing瘤病理报告中提取了预后组织学特征. 神经元特异性内酶 (NSE) 表示更高的风险,而S100则表明更好的生存率,特别是在局部疾病中.

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Last Updated: Jun 9, 2026

Predictive Immune Modeling of Solid Tumors

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
人工智能在医学中的应用

背景情况:

目前的尤文肉瘤风险分层主要使用临床因素,如转移状态.
组织学异质性是一种潜在的预后指标,但很难从非结构化的病理学报告中提取出来.
大规模的回顾性分析受到从历史临床试验文件中手动提取数据的劳动密集性限制.

研究的目的:

验证大型语言模型 (LLM) 的实用性,用于从病理学报告中进行可扩展的数据抽象.
在一个大型的,多机构的尤宁肉瘤患者队列中确定预后组织学特征.
评估人工智能衍生的组织学数据对Ewing肉瘤风险分层的改进影响.

主要方法:

六个儿童瘤组 (COG) 临床试验中的931名患者的回顾性队列研究.
利用基于LLM的管道从数字化病理报告中提取免疫组织化学 (IHC) 标记物和CD99染色模式.
经过验证的LLM提取精度与人类注释的地面真相和高级专家相对应;使用生存分析评估预后价值.

主要成果:

该LLM在交叉验证中实现了98.1%的准确性,超过了人类注释者.
神经特异性内酶 (NSE) 的阳性与明显较低的整体存活率 (OS) (HR 2.15) 相关,特别是在非转移性疾病 (HR 5.64) 中.
阳性S100与改善的OS相关 (HR0.58).

结论:

病理学变量的LLM辅助提取是准确的,可扩展的,并且可以从历史临床试验中解锁有价值的"黑暗数据".
NSE和S100被确定为尤宁肉瘤的显著预后生物标志物,特别是在局部疾病中.
人工智能衍生的组织学数据可以完善风险分层,并应考虑未来的潜在临床试验.