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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

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Utilising Natural Language Processing to Identify Brain Tumor Patients for Clinical Trials: Development and Initial

James Booker¹, Jack Penn¹, Kawsar Noor²

¹Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.

World Neurosurgery

|March 20, 2025

Summary

Natural Language Processing (NLP) effectively extracts brain tumor diagnoses from electronic health records. This facilitates linking patients to relevant clinical trials, improving trial recruitment efficiency.

Keywords:

Brain tumors Clinical trials Machine learning Natural language processing Neuro-oncology Recruitment

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Area of Science:

Medical Informatics
Clinical Research Informatics
Natural Language Processing

Background:

Clinical trial eligibility screening is resource-intensive.
Natural Language Processing (NLP) models can extract data from Electronic Health Records (EHRs) to improve screening.
Automating data extraction can streamline the identification of eligible patients.

Purpose of the Study:

To evaluate an NLP model's ability to extract brain tumor diagnoses from outpatient clinic letters.
To assess the feasibility of linking extracted diagnoses to ongoing clinical trials.
To determine the accuracy and relevance of NLP-identified clinical trial matches.

Main Methods:

Retrospective cohort study of neuro-oncology clinic letters.
Utilized an NLP model for Named Entity Recognition and Linking to the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) ontology.
Human annotators validated extracted concepts and trial relevance; results displayed on an EHR-integrated dashboard.

Main Results:

The NLP model achieved high performance: macro-precision = 0.994, macro-recall = 0.964, macro-F1 = 0.977.
Identified 399 medical concepts from 196 clinic letters.
Linked to 1417 trials, with 755 highly relevant matches for patients meeting eligibility criteria.

Conclusions:

NLP effectively extracts brain tumor diagnoses from free-text EHR data with minimal training.
Extracted information can be linked to relevant clinical trials, suggesting integration into clinical workflows.
Further research is needed to evaluate the impact on clinical outcomes.