Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study

Laura Satchwell, Linda Wedlake, Emily Greenlay, Xingfeng Li, Christina Messiou, Ben Glocker, Tara Barwick, Theodore Barfoot, Simon Doran, Martin O Leach, Dow Mu Koh, Martin Kaiser, Stefan Winzeck, Talha Qaiser, Eric Aboagye, Andrea Rockall

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of multiple myeloma. Reporting WB-MRI scans requires expertise to interpret and can be challenging for radiologists who need to meet rapid turn-around requirements. Automated computational tools based on machine learning (ML) could assist the radiologist in terms of sensitivity and reading speed and would facilitate improved accuracy, productivity and cost-effectiveness. The MALIMAR study aims to develop and validate a ML algorithm to increase the diagnostic accuracy and reading speed of radiological interpretation of WB-MRI compared with standard methods. This phase II/III imaging trial will perform retrospective analysis of previously obtained clinical radiology MRI scans and scans from healthy volunteers obtained prospectively to implement training and validation of an ML algorithm. The study will comprise three project phases using approximately 633 scans to (1) train the ML algorithm to identify active disease, (2) clinically validate the ML algorithm and (3) determine change in disease status following treatment via a quantification of burden of disease in patients with myeloma. Phase 1 will primarily train the ML algorithm to detect active myeloma against an expert assessment ('reference standard'). Phase 2 will use the ML output in the setting of radiology reader study to assess the difference in sensitivity when using ML-assisted reading or human-alone reading. Phase 3 will assess the agreement between experienced readers (with and without ML) and the reference standard in scoring both overall burden of disease before and after treatment, and response. MALIMAR has ethical approval from South Central-Oxford C Research Ethics Committee (REC Reference: 17/SC/0630). IRAS Project ID: 233501. CPMS Portfolio adoption (CPMS ID: 36766). Participants gave informed consent to participate in the study before taking part. MALIMAR is funded by National Institute for Healthcare Research Efficacy and Mechanism Evaluation funding (NIHR EME Project ID: 16/68/34). Findings will be made available through peer-reviewed publications and conference dissemination. NCT03574454. [Abstract copyright: © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.]
Original languageEnglish
Pages (from-to)e067140
JournalBMJ Open
Volume12
Issue number10
Early online date5 Oct 2022
DOIs
Publication statusE-pub ahead of print - 5 Oct 2022

Keywords

  • ONCOLOGY
  • Cross-Sectional Studies
  • Whole Body Imaging - methods
  • Sulfides
  • Myeloma
  • Machine Learning
  • Diagnostic radiology
  • Magnetic resonance imaging
  • Magnetic Resonance Imaging - methods
  • Clinical Trials, Phase III as Topic
  • Retrospective Studies
  • Diagnostic Tests, Routine
  • Chlorobenzenes
  • Humans
  • Clinical Trials, Phase II as Topic
  • Multiple Myeloma - diagnostic imaging - therapy

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