An Introduction to Natural Language Processing Techniques and Framework for Clinical Implementation in Radiation Oncology

Natural Language Processing (NLP) is a key technique for developing Medical Artificial Intelligence (AI) systems that leverage Electronic Health Record (EHR) data to build diagnostic and prognostic models. NLP enables the conversion of unstructured clinical text into structured data that can be fed into AI algorithms. The emergence of the transformer architecture and large language models (LLMs) has led to remarkable advances in NLP for various healthcare tasks, such as entity recognition, relation extraction, sentence similarity, text summarization, and question answering. In this article, we review the major technical innovations that underpin modern NLP models and present state-of-the-art NLP applications that employ LLMs in radiation oncology research. However, these LLMs are prone to many errors such as hallucinations, biases, and ethical violations, which necessitate rigorous evaluation and validation before clinical deployment. As such, we propose a comprehensive framework for assessing the NLP models based on their purpose and clinical fit, technical performance, bias and trust, legal and ethical implications, and quality assurance, prior to implementation in clinical radiation oncology. Our article aims to provide guidance and insights for researchers and clinicians who are interested in developing and using NLP models in clinical radiation oncology.

FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images

Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of prostate derived from CTs poor soft tissue contrast, and (2) the limitation of convolutional neural network based models in capturing long-range global context. Here we propose a focal transformer based image segmentation architecture to effectively and efficiently extract local visual features and global context from CT images. Furthermore, we design a main segmentation task and an auxiliary boundary-induced label regression task as regularization to simultaneously optimize segmentation results and mitigate the unclear boundary effect, particularly in unseen data set. Extensive experiments on a large data set of 400 prostate CT scans demonstrate the superior performance of our focal transformer to the competing methods on the prostate segmentation task.