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

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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.