# RadLex Normalization in Radiology Reports

arXiv: https://arxiv.org/abs/2009.05128 · HTML: https://mlsanity.com/paper/2009.05128/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model](https://mlsanity.com/paper/2108.09211/) (2021)
- [A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models](https://mlsanity.com/paper/2403.18975/) (2024)
- [HeadCT-ONE: Enabling Granular and Controllable Automated Evaluation of Head CT Radiology Report Generation](https://mlsanity.com/paper/2409.13038/) (2024)
- [Learning Semi-Structured Representations of Radiology Reports](https://mlsanity.com/paper/2112.10746/) (2021)

## Abstract (arXiv)

Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities to RadLex terms. We manually construct a normalization corpus by annotating entities from three types of reports. This contains 1706 entity mentions. We propose two deep learning-based NLP methods based on a pre-trained language model (BERT) for automatic normalization. First, we employ BM25 to retrieve candidate concepts for the BERT-based models (re-ranker and span detector) to predict the normalized concept. The results are promising, with the best accuracy (78.44%) obtained by the span detector. Additionally, we discuss the challenges involved in corpus construction and propose new RadLex terms.