由于癌症是全球死亡的主要原因，流行病学和临床癌症登记对于加强肿瘤护理和促进科学研究至关重要。然而，医疗数据的异构性对当前肿瘤记录的手动过程提出了重大挑战。本文探讨了大型语言模型 (LLM) 将非结构化医疗报告转换为德国基础肿瘤学数据集规定的结构化格式的潜力。我们的研究结果表明，将法学硕士整合到现有的医院数据管理系统或癌症登记处可以显着提高癌症数据收集的质量和完整性——这是诊断和治疗癌症以及提高治疗效果和益处的重要组成部分。这项工作有助于就人工智能或法学硕士彻底改变一般医疗数据处理和报告，特别是癌症护理的潜力进行更广泛的讨论。

With cancer being a leading cause of death globally, epidemiological and clinical cancer registration is paramount for enhancing oncological care and facilitating scientific research. However, the heterogeneous landscape of medical data presents significant challenges to the current manual process of tumor documentation. This paper explores the potential of Large Language Models (LLMs) for transforming unstructured medical reports into the structured format mandated by the German Basic Oncology Dataset. Our findings indicate that integrating LLMs into existing hospital data management systems or cancer registries can significantly enhance the quality and completeness of cancer data collection - a vital component for diagnosing and treating cancer and improving the effectiveness and benefits of therapies. This work contributes to the broader discussion on the potential of artificial intelligence or LLMs to revolutionize medical data processing and reporting in general and cancer care in particular.