Abstract
Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using real-world data face challenges, including the absence of explicit phenotypes and treatment outcome labels such as cancer progression and toxicity. This study addresses these challenges by employing Large Language Models (LLMs) and ontology-based techniques for phenotypes and outcome label extraction from patient notes. We focused on one of the most frequently occurring cancers, breast cancer, due to its high prevalence and significant variability in patient response to treatment, making it a critical area for improving predictive modeling. The dataset included features such as vitals, demographics, staging, biomarkers, and performance scales. Drug regimens and their combinations were extracted from the chemotherapy plans in the EMR data and shortlisted based on NCCN guidelines, verified with NIH standards, and analyzed through survival modeling. The proposed approach significantly reduced phenotypes sparsity and improved predictive accuracy. Random Survival Forest was used to predict time-to-failure, achieving a C-index of 73%, and utilized as a classifier at a specific time point to predict treatment outcomes, with accuracy and F1 scores above 70%. The outcome probabilities were validated for reliability by calibration curves. We extended our approach to four other cancer types. This research highlights the potential of early prediction of treatment outcomes using LLM-based clinical data extraction enabling personalized treatment plans with better patient outcomes.
Abstract (translated)
化疗作为癌症治疗的一种方法，成本高昂且伴有严重的副作用，这突显了早期预测治疗效果以改进患者管理和辅助知情决策的迫切需求。利用真实世界数据构建化疗疗效预测模型面临着挑战，例如缺乏明确的表型和治疗结果标签（如癌症进展和毒性）。本研究通过使用大型语言模型（LLMs）和基于本体的技术从病历笔记中提取表型和结果标签来解决这些问题。我们重点关注乳腺癌这一最常见的癌症之一，因为它的高发病率以及患者对治疗反应的高度变异性使其成为改进预测建模的关键领域。 数据集包括生命体征、人口统计学信息、分期、生物标志物及表现评分等特征。从电子病历（EMR）中的化疗计划中提取药物方案及其组合，并根据NCCN指南筛选，与NIH标准验证并进行生存分析模型的分析。所提出的方法显著减少了表型稀疏性并提高了预测准确性。 随机生存森林被用来预测失效时间，达到了C指数73%，并在特定时间点作为分类器来预测治疗效果，准确率和F1分数均超过70%。通过校准曲线验证了结果的概率可靠性。 我们进一步将这种方法扩展到了另外四种癌症类型上。这项研究强调了利用基于LLM的临床数据提取技术实现早期治疗效果预测的潜力，这有助于制定个性化的治疗计划并提高患者预后质量。
URL
https://arxiv.org/abs/2603.11594
