The reporting quality of natural language processing studies: systematic review of studies of radiology reports

A scoping review of large language model based approaches for information extraction from radiology reports

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports. We conduct a scoping review that follows the PRISMA-ScR guideline. Queries of five databases were conducted on August 1st 2023. Among the 34 studies that met inclusion criteria, only pre-transformer and encoder-based models are described. External validation shows a general performance decrease, although LLMs might improve generalizability of IE approaches. Reports related to CT and MRI examinations, as well as thoracic reports, prevail. Most common challenges reported are missing validation on external data and augmentation of the described methods. Different reporting granularities affect the comparability and transparency of approaches.

Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review

INTRODUCTION

Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking.

AREAS COVERED

This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications.

EXPERT OPINION

Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.

Artificial Intelligence in Radiology: Opportunities and Challenges

Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

OBJECTIVES

Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for smaller radiology report datasets.

METHODS

We compared the performance of BioBERT, a deep-learning-based transformer model pre-trained on biomedical text, and three traditional machine-learning models (gradient boosted tree, random forest, and logistic regression) on seven classification tasks given free-text radiology reports. Tasks included detection of appendicitis, diverticulitis, bowel obstruction, and enteritis/colitis on abdomen/pelvis CT reports, ischemic infarct on brain CT/MRI reports, and medial and lateral meniscus tears on knee MRI reports (7,204 total annotated reports). The performance of NLP models on held-out test sets was compared after training using the full training set, and 2.5%, 10%, 25%, 50%, and 75% random subsets of the training data.

RESULTS

In all tested classification tasks, BioBERT performed poorly at smaller training sample sizes compared to non-deep-learning NLP models. Specifically, BioBERT required training on approximately 1,000 reports to perform similarly or better than non-deep-learning models. At around 1,250 to 1,500 training samples, the testing performance for all models began to plateau, where additional training data yielded minimal performance gain.

CONCLUSIONS

With larger sample sizes, transformer NLP models achieved superior performance in radiology report binary classification tasks. However, with smaller sizes (<1000) and more imbalanced training data, traditional NLP techniques performed better.

ADVANCES IN KNOWLEDGE

Our benchmarks can help guide clinical NLP researchers in selecting machine-learning models according to their dataset characteristics.

Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal

OBJECTIVES

To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology.

METHODS

A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach's alpha (Cα) correlation coefficient.

RESULTS

The final SR form included 118 items (6 in the "Patient Clinical Data" section, 4 in the "Clinical Evaluation" section, 9 in the "Imaging Protocol" section, and 99 in the "Report" section). The experts' overall mean score and sum of scores were 4.77 (range 1-5) and 257.56 (range 206-270) in the first Delphi round, and 4.96 (range 4-5) and 208.44 (range 200-210) in the second round, respectively. In the second Delphi round, the experts' overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87).

CONCLUSIONS

Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.

A scoping review of natural language processing of radiology reports in breast cancer

Various natural language processing (NLP) algorithms have been applied in the literature to analyze radiology reports pertaining to the diagnosis and subsequent care of cancer patients. Applications of this technology include cohort selection for clinical trials, population of large-scale data registries, and quality improvement in radiology workflows including mammography screening. This scoping review is the first to examine such applications in the specific context of breast cancer. Out of 210 identified articles initially, 44 met our inclusion criteria for this review. Extracted data elements included both clinical and technical details of studies that developed or evaluated NLP algorithms applied to free-text radiology reports of breast cancer. Our review illustrates an emphasis on applications in diagnostic and screening processes over treatment or therapeutic applications and describes growth in deep learning and transfer learning approaches in recent years, although rule-based approaches continue to be useful. Furthermore, we observe increased efforts in code and software sharing but not with data sharing.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Rule-based natural language processing for automation of stroke data extraction: a validation study

PURPOSE

Data extraction from radiology free-text reports is time consuming when performed manually. Recently, more automated extraction methods using natural language processing (NLP) are proposed. A previously developed rule-based NLP algorithm showed promise in its ability to extract stroke-related data from radiology reports. We aimed to externally validate the accuracy of CHARTextract, a rule-based NLP algorithm, to extract stroke-related data from free-text radiology reports.

METHODS

Free-text reports of CT angiography (CTA) and perfusion (CTP) studies of consecutive patients with acute ischemic stroke admitted to a regional stroke center for endovascular thrombectomy were analyzed from January 2015 to 2021. Stroke-related variables were manually extracted as reference standard from clinical reports, including proximal and distal anterior circulation occlusion, posterior circulation occlusion, presence of ischemia or hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status. These variables were simultaneously extracted using a rule-based NLP algorithm. The NLP algorithm's accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were assessed.

RESULTS

The NLP algorithm's accuracy was > 90% for identifying distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS. Accuracy was 85%, 74%, and 79% for proximal anterior circulation occlusion, presence of ischemia, and collateral status respectively. The algorithm confirmed the absence of variables from radiology reports with an 87-100% accuracy.

CONCLUSIONS

Rule-based NLP has a moderate to good performance for stroke-related data extraction from free-text imaging reports. The algorithm's accuracy was affected by inconsistent report styles and lexicon among reporting radiologists.

A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data

Intelligent fault diagnosis methods based on deep learning have achieved much progress in recent years. However, there are two major factors causing serious degradation of the performance of these algorithms in real industrial applications, i.e., limited labeled training data and complex working conditions. To solve these problems, this study proposed a domain generalization-based hybrid matching network utilizing a matching network to diagnose the faults using features encoded by an autoencoder. The main idea was to regularize the feature extractor of the network with an autoencoder in order to reduce the risk of overfitting with limited training samples. In addition, a training strategy using dropout with random changing rates on inputs was implemented to enhance the model’s generalization on unseen domains. The proposed method was validated on two different datasets containing artificial and real faults. The results showed that considerable performance was achieved by the proposed method under cross-domain tasks with limited training samples.

Use of Natural Language Processing (NLP) in Evaluation of Radiology Reports: An Update on Applications and Technology Advances

Natural language processing (NLP) is focused on the computer interpretation of human language and can be used to evaluate radiology reports and has demonstrated useful applications in essentially all aspects of medical imaging delivery: interpretation of imaging data, improving image acquisition, image analysis, and increasing efficiency of imaging services. This manuscript reviews general technologic approaches to NLP at a level hopefully understandable by clinical radiologists, discusses recent advancements in NLP techniques, and discusses current and potential applications of NLP in radiology.