CELL INTELLIGENCE · 肿瘤细胞治疗研究

CELLIN 肿瘤细胞治疗研究

← 返回

SEETrials：利用大语言模型提取肿瘤临床试验中的安全性和疗效

英文原题：SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

查看英文原题

SEETrials: Leveraging Large Language Models for Safety and Efficacy Extraction in Oncology Clinical Trials.

PubMed 2024/05/13（内容时间） medRxiv

分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。

技术：CAR-T技术：双特异性抗体多发性骨髓瘤淋巴瘤白血病
登录后收藏与关注 全文解读登录并开通会员后使用加入对比登录并开通会员后使用

研究概要

SEETrials 在不同治疗药物和多种癌症领域均展现出高度准确的数据提取能力和通用性。

中文摘要

背景

肿瘤临床试验结果的初步信息常需从会议摘要中人工获取。我们旨在开发一种自动化系统，以高精度、精细粒度地从研究摘要中提取安全性和疗效信息，并将其转换为可计算数据，以支持及时临床决策。

方法

我们收集了重要会议及 PubMed（2012–2023 年）的临床试验摘要。SEETrials 系统由四个模块构成：预处理、提示建模、知识摄取和后处理。我们定性和定量评估了系统性能，并考察其在多发性骨髓瘤（MM）、乳腺癌、肺癌、淋巴瘤和白血病等不同癌种中的泛化能力。此外，我们大规模分析了临床试验研究中 MM 新型疗法（包括 CAR-T、双特异性抗体和抗体药物偶联物（ADC））的疗效及安全性。

结果

SEETrials 对 MM 试验中的 70 项数据元素取得较高精确率（0.958）、召回率（敏感度，0.944）和 F1 分数（0.951）。对另外四种癌症的泛化测试中，精确率、召回率和 F1 分数均在 0.966–0.986 范围内。不同疗法的安全性和疗效相关实体分布存在差异，某些不良事件在特定治疗中更常见。以总缓解率（ORR）和完全缓解（CR）进行比较，显示不同疗法间存在差异：CAR-T（ORR：88%，95% CI：84%–92%；CR：95%，95% CI：53%–66%），双特异性抗体（ORR：64%，95% CI：55%–73%；CR：27%，95% CI：16%–37%），ADC（ORR：51%，95% CI：37%–65%；CR：26%，95% CI：1%–51%）。多个治疗亚组中分析的结局指标存在显著研究异质性（I² 异质性指数 >75%）。

结论

SEETrials 展现出高度准确的数据提取能力，并可灵活应用于不同疗法及癌症领域。其对大型数据集的自动化处理有助于进行细致比较，促进临床见解快速有效传播。

展开英文摘要原文
BACKGROUND

Initial insights into oncology clinical trial outcomes are often gleaned manually from conference abstracts. We aimed to develop an automated system to extract safety and efficacy information from study abstracts with high precision and fine granularity, transforming them into computable data for timely clinical decision-making.

METHODS

We collected clinical trial abstracts from key conferences and PubMed (2012-2023). The SEETrials system was developed with four modules: preprocessing, prompt modeling, knowledge ingestion and postprocessing. We evaluated the system's performance qualitatively and quantitatively and assessed its generalizability across different cancer types- multiple myeloma (MM), breast, lung, lymphoma, and leukemia. Furthermore, the efficacy and safety of innovative therapies, including CAR-T, bispecific antibodies, and antibody-drug conjugates (ADC), in MM were analyzed across a large scale of clinical trial studies.

RESULTS

SEETrials achieved high precision (0.958), recall (sensitivity) (0.944), and F1 score (0.951) across 70 data elements present in the MM trial studies Generalizability tests on four additional cancers yielded precision, recall, and F1 scores within the 0.966-0.986 range. Variation in the distribution of safety and efficacy-related entities was observed across diverse therapies, with certain adverse events more common in specific treatments. Comparative performance analysis using overall response rate (ORR) and complete response (CR) highlighted differences among therapies: CAR-T (ORR: 88%, 95% CI: 84-92%; CR: 95%, 95% CI: 53-66%), bispecific antibodies (ORR: 64%, 95% CI: 55-73%; CR: 27%, 95% CI: 16-37%), and ADC (ORR: 51%, 95% CI: 37-65%; CR: 26%, 95% CI: 1-51%). Notable study heterogeneity was identified (>75% I 2 heterogeneity index scores) across several outcome entities analyzed within therapy subgroups.

CONCLUSION

SEETrials demonstrated highly accurate data extraction and versatility across different therapeutics and various cancer domains. Its automated processing of large datasets facilitates nuanced data comparisons, promoting the swift and effective dissemination of clinical insights.

论文信息

作者
Lee K、Paek H、Huang LC、Hilton CB、Datta S、Higashi J、Ofoegbu N、Wang J
文献类型
预印本
期刊
medRxiv : the preprint server for health sciences2024 May 13
原文标识
PubMed 38798420　·　DOI 10.1101/2024.01.18.24301502

在 PubMed 查看 → 出版商原文（DOI） 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →