Annually and globally, over three billion radiography examinations and
computer tomography scans result in mostly unstructured radiology reports
containing free text. Despite the potential benefits of structured reporting,
its adoption is limited by factors such as established processes, resource
constraints and potential loss of information. However, structured information
would be necessary for various use cases, including automatic analysis,
clinical trial matching, and prediction of health outcomes. This study
introduces RadEx, an end-to-end framework comprising 15 software components and
ten artifacts to develop systems that perform automated information extraction
from radiology reports. It covers the complete process from annotating training
data to extracting information by offering a consistent generic information
model and setting boundaries for model development. Specifically, RadEx allows
clinicians to define relevant information for clinical domains (e.g.,
mammography) and to create report templates. The framework supports both
generative and encoder-only models and the decoupling of information extraction
from template filling enables independent model improvements. Developing
information extraction systems according to the RadEx framework facilitates
implementation and maintenance as components are easily exchangeable, while
standardized artifacts ensure interoperability between components.

RadEx 是一个端到端框架，包括 15 个软件组件和 10 个工件，用于从放射学报告中进行自动化信息提取，支持医生定义临床领域相关信息和创建报告模板，实现信息提取系统的快速实施与维护。

RadEx：基于大型语言模型的放射学报告结构化信息提取框架

RadEx: A Framework for Structured Information Extraction from Radiology  Reports based on Large Language Models

BACKGROUND: Radiology reports are typically written in a free-text format,
making clinical information difficult to extract and use. Recently the adoption
of structured reporting (SR) has been recommended by various medical societies
thanks to the advantages it offers, e.g. standardization, completeness and
information retrieval. We propose a pipeline to extract information from
free-text radiology reports, that fits with the items of the reference SR
registry proposed by a national society of interventional and medical
radiology, focusing on CT staging of patients with lymphoma. METHODS: Our work
aims to leverage the potential of Natural Language Processing (NLP) and
Transformer-based models to deal with automatic SR registry filling. With the
availability of 174 radiology reports, we investigate a rule-free generative
Question Answering approach based on a domain-specific version of T5 (IT5). Two
strategies (batch-truncation and ex-post combination) are implemented to comply
with the model's context length limitations. Performance is evaluated in terms
of strict accuracy, F1, and format accuracy, and compared with the widely used
GPT-3.5 Large Language Model. A 5-point Likert scale questionnaire is used to
collect human-expert feedback on the similarity between medical annotations and
generated answers. RESULTS: The combination of fine-tuning and batch splitting
allows IT5 to achieve notable results; it performs on par with GPT-3.5 albeit
its size being a thousand times smaller in terms of parameters. Human-based
assessment scores show a high correlation (Spearman's correlation
coefficients>0.88, p-values<0.001) with AI performance metrics (F1) and confirm
the superior ability of LLMs (i.e., GPT-3.5, 175B of parameters) in generating
plausible human-like statements.

自由文本放射学报告中提取信息的流程，以及使用自然语言处理和基于 Transformer 的模型填充结构化报告注册表的性能评估和比较。

用生成变换器将自由文本放射学记录重塑为结构化报告

Reshaping Free-Text Radiology Notes Into Structured Reports With  Generative Transformers

Radiology reporting is a crucial part of the communication between
radiologists and other medical professionals, but it can be time-consuming and
error-prone. One approach to alleviate this is structured reporting, which
saves time and enables a more accurate evaluation than free-text reports.
However, there is limited research on automating structured reporting, and no
public benchmark is available for evaluating and comparing different methods.
To close this gap, we introduce Rad-ReStruct, a new benchmark dataset that
provides fine-grained, hierarchically ordered annotations in the form of
structured reports for X-Ray images. We model the structured reporting task as
hierarchical visual question answering (VQA) and propose hi-VQA, a novel method
that considers prior context in the form of previously asked questions and
answers for populating a structured radiology report. Our experiments show that
hi-VQA achieves competitive performance to the state-of-the-art on the medical
VQA benchmark VQARad while performing best among methods without
domain-specific vision-language pretraining and provides a strong baseline on
Rad-ReStruct. Our work represents a significant step towards the automated
population of structured radiology reports and provides a valuable first
benchmark for future research in this area. We will make all annotations and
our code for annotation generation, model evaluation, and training publicly
available upon acceptance. Our dataset and code is available at
this https URL

本研究提出了一个新的基准数据集 Rad-ReStruct，通过采用分层结构化报告的形式，提供了细粒度、分层次的注释，以用于支持医学影像元数据的自动填充以及加快放射学报告的编写速度，并通过开发一种新颖的分层视觉问答模型 hi-VQA 实现进行项目分析。