To investigate Natural Language Processing combined with AI enabled image analysis tools applied to PETCT reporting to enhance specificity in follow-u

研究自然语言处理与人工智能图像分析工具相结合应用于 PETCT 报告，以增强后续报告的特异性

• 批准号: 2698750
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2698750
• 关键词: investigate; Natural; Language; Processing; combined

The goal of this study is to use Natural Language Processing (NLP) to extract information from PET-CT reports for cancer. PET-CT reports use complex, subject-specific language which potentially needs to be ready and understood by other Radiologists, Oncology Clinicians and Patients. These groups have different levels of expertise and ascertaining the overall judgement of a report can be unclear. This study seeks to use past reports to build an AI classifier which can label future reports based on TNM cancer staging criteria. The training data is annotated at report level to better take into account the treatment context. There are separate document-level classifications for Tumour, Node and Metastasis findings. Incidental findings are also being annotated to see if these can be extracted at span-level as these clinical details can be missed due to the free text format of reports.Success in extracting these labels and findings would allow for easier construction of datasets for other projects, and other healthcare analysis. In particular: The reliable extraction of 'normal' scans (with no cancerous abnormalities) could help further research by ascertaining image features that reflect this classification. We will also seek to explore how recent breakthroughs in NLP can improve radiology report summarisation (or automatic impression generation) as this task has not been explored thoroughly with regard to PET-CT.The methodology is experimental; testing the design and application of the models against accepted evaluation metrics. Precision, Recall and F1 score for each class would be essential to demonstrate its effectiveness. Area under the receiver operating curve should also be used for evaluation as it uses the probabilities of the classes, as opposed to the final classification alone. Rouge a text summarisation metric will also be used to evaluate incidental findings and impression generations. I will also explore potential human evaluation techniques.

本研究的目的是使用自然语言处理（NLP）从PET-CT报告中提取癌症信息。PET-CT报告使用复杂的、特定学科的语言，可能需要其他放射科医生、肿瘤临床医生和患者准备好并理解。这些小组具有不同水平的专业知识，确定一份报告的总体判断可能不明确。本研究旨在利用过去的报告建立一个人工智能分类器，该分类器可以根据TNM癌症分期标准标记未来的报告。训练数据在报告级别进行注释，以便更好地考虑到治疗背景。肿瘤、淋巴结和转移的发现有单独的文档级分类。由于报告的自由文本格式可能会遗漏这些临床细节，因此对偶然发现也进行了注释，以查看是否可以在跨度级别提取这些细节。成功地提取这些标签和发现，可以更容易地为其他项目和其他医疗保健分析构建数据集。特别是：通过确定反映这种分类的图像特征，可靠地提取“正常”扫描（没有癌症异常）可以帮助进一步的研究。我们还将探索NLP的最新突破如何改善放射学报告总结（或自动印象生成），因为这项任务尚未在PET-CT方面进行彻底的探索。该方法是实验性的；根据可接受的评估标准测试模型的设计和应用。准确率、召回率和每一类的F1分数是证明其有效性的必要条件。接受者操作曲线下的面积也应该用于评估，因为它使用了类别的概率，而不是单独的最终分类。胭脂文本摘要度量也将用于评估偶然发现和印象世代。我还将探讨潜在的人类评估技术。