Large language models (LLMs) have demonstrated potential in the innovation of many disciplines. However, how they can best be developed for oncology remains underdeveloped. State-of-the-art OpenAI models were fine-tuned on a clinical dataset and clinical guidelines text corpus for two important cancer treatment factors, adjuvant radiation therapy and chemotherapy, using a novel Langchain prompt engineering pipeline. A high accuracy (0.85+) was achieved in the classification of adjuvant radiation therapy and chemotherapy for breast cancer patients. Furthermore, a confidence interval was formed from observational data on the quality of treatment from human oncologists to estimate the proportion of scenarios in which the model must outperform the original oncologist in its treatment prediction to be a better solution overall as 8.2% to 13.3%. Due to indeterminacy in the outcomes of cancer treatment decisions, future investigation, potentially a clinical trial, would be required to determine if this threshold was met by the models. Nevertheless, with 85% of U.S. cancer patients receiving treatment at local community facilities, these kinds of models could play an important part in expanding access to quality care with outcomes that lie, at minimum, close to a human oncologist.

翻译：大型语言模型（LLMs）已在多学科创新中展现出潜力，但其在肿瘤学领域的最佳开发方式仍有待探索。本研究采用一种新颖的Langchain提示工程流程，基于临床数据集和临床指南文本语料库，对最先进的OpenAI模型进行了针对辅助放射治疗和化疗这两个关键癌症治疗因素的微调。该模型在乳腺癌患者辅助放射治疗与化疗的分类任务中取得了较高准确率（0.85以上）。此外，通过整合人类肿瘤医师治疗质量的观测数据构建置信区间，估算出模型需在8.2%至13.3%的临床场景中其治疗预测优于原主治医师，方能成为整体更优的解决方案。鉴于癌症治疗决策结果的不确定性，未来需通过进一步研究（可能包括临床试验）来验证模型是否达到该阈值。值得注意的是，当前美国85%的癌症患者在地方社区医疗机构接受治疗，此类模型至少能在提供接近人类肿瘤医师水平的诊疗结果方面，为扩大优质医疗服务的可及性发挥重要作用。