Structural MRI of the brain is routinely performed on patients referred to memory clinics; however, resulting radiology reports, including volumetric assessments, are conventionally stored as unstructured free text. We sought to use natural language processing (NLP) to extract text relating to intracranial volumetric assessment from brain MRI text reports to enhance routine data availability for research purposes.

Electronic records from a large mental healthcare provider serving a geographic catchment of 1.3 million residents in four boroughs of south London, UK.

A corpus of 4007 de-identified brain MRI reports from patients referred to memory assessment services. An NLP algorithm was developed, using a span categorisation approach, to extract six binary (presence/absence) categories from the text reports: (i) global volume loss, (ii) hippocampal/medial temporal lobe volume loss and (iii) other lobar/regional volume loss. Distributions of these categories were evaluated.

The overall F1 score for the six categories was 0.89 (precision 0.92, recall 0.86), with the following precision/recall for each category: presence of global volume loss 0.95/0.95, absence of global volume loss 0.94/0.77, presence of regional volume loss 0.80/0.58, absence of regional volume loss 0.91/0.93, presence of hippocampal volume loss 0.90/0.88, and absence of hippocampal volume loss 0.94/0.92.

These results support the feasibility and accuracy of using NLP techniques to extract volumetric assessments from radiology reports, and the potential for automated generation of novel meta-data from dementia assessments in electronic health records.

Rare diseases (RD) are collectively common and often genetic. Families value and can benefit from precise molecular diagnoses. Prolonged diagnostic odysseys exacerbate the burden of RD on patients, families and the healthcare system. Genome sequencing (GS) is a near-comprehensive test for genetic RD, but existing care models—where consultation with a medical geneticist is a prerequisite for testing—predate GS and may limit access or delay diagnosis. Evidence is needed to guide the optimal positioning of GS in care pathways. While initiating GS prior to geneticist consultation has been trialled in acute care settings, there are no data to inform the utility of this approach in outpatient care, where most patients with RD seek genetics services. We aim to evaluate the diagnostic yield, time to diagnosis, clinical and personal utility and incremental cost-effectiveness of GS initiated at the time of referral triage (pre-geneticist evaluation) compared with standard of care.

200 paediatric patients referred to one of two large genetics centres in Ontario, Canada, for suspected genetic RD will be randomised into a 1:1 ratio to the intervention (GS first) or standard of care (geneticist first) arm. An unblinded, permuted block randomisation design will be used, stratified within each recruitment site by phenotype and prior genetic testing. The primary outcome measure is time to genetic diagnosis or to cessation of active follow-up. Survival analysis will be used to analyse time-to-event data. Additional measures will include patient-reported and family-reported measures of satisfaction, understanding and perceived test utility, clinician-reported measures of perceived test utility and management impact, and healthcare system utilisation and costs.

This study was approved by Clinical Trials Ontario. Results will be disseminated, at minimum, via peer-reviewed journals, professional conferences and internal reports to funding bodies. Efforts will be made to share aggregated study results with participants and their families.

NCT06935019.