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Patient attrition in Molecular Tumour Boards: a systematic review
Open accessPublished version of a preprint
Abstract
Background: Molecular Tumour Boards (MTBs) were created with the purpose of supporting clinical decision-making within precision medicine. Though in use globally, reporting on these meetings often focuses on the small percentages of patients that receive treatment via this process and are less likely to report on, and assess, patients who do not receive treatment. Methods: A literature review was performed to understand patient attrition within MTBs and barriers to patients receiving treatment. A total of 51 papers were reviewed spanning a 6-year period from 11 different countries. Results: In total, 20% of patients received treatment through the MTB process. Of those that did not receive treatment, the main reasons were no mutations identified (27%), no actionable mutations (22%) and clinical deterioration (15%). However, data were often incomplete due to inconsistent reporting of MTBs with only 55% reporting on patients having no mutations, 55% reporting on the presence of actionable mutations with no treatment options and 59% reporting on clinical deterioration. Discussion: As patient attrition in MTBs is an issue which is very rarely alluded to in reporting, more transparent reporting is needed to understand barriers to treatment and integration of new technologies is required to process increasing omic and treatment data.
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Emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies.
“Similarly, emerging evidence suggests that AI-enabled applications can improve operational efficiency, clinical decision-making, patient triage, scheduling, and treatment planning when supported by appropriate governance, infrastructure, and workforce competencies”
From A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling (2026) · cites this paper as [48]
The Discussion’s “Points of intervention” section says AI and machine learning can help evaluate patients for studies, and trial-matching software can help clinicians review options based on patient profiles. This supports a narrower claim about patient selection and clinical decision support in molecular tumour boards. It does not address scheduling or make the broader claim about operational efficiency conditional on governance, infrastructure, and workforce competencies. (LLM, conf=0.86)
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Metadata
- Authors
- Frost, H., Graham, D. M., Carter, L., O’Regan, P., Landers, D., & Freitas, A.
- Affiliations
- University of Manchester (GB)
- Cancer Research UK Manchester Institute (GB)
- Corresponding author
- Frost, Hannah
- Venue
- British Journal of Cancer
- Published
- 2022-08-08
- Volume
- 127
- Issue
- 8
- Pages
- 1557-1564
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC9553981
- OpenAlex
- W4290724595
- Cited by
- 17
- References
- 86
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 2 · 2023: 3 · 2022: 1
- FWCI
- 0.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- British Journal of Cancer (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (publishedVersion, other-oa)
- Research Explorer (The University of Manchester) (submittedVersion)
- Funders
- AstraZeneca (119106)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- molecular tumor board, patient dropout, precision oncology, actionable mutations, barriers to treatment, clinical deterioration, clinical decision-making, multi-omics integration
- MeSH terms
- Clinical Decision-Making, Clinical Deterioration, Humans, Neoplasms, Precision Medicine
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