Title: MAchine Learning In MyelomA Response (MALIMAR study): Development of machine learning support for reading whole body diffusion weighted magnetic resonance imaging (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment
Funder: Department of Health and Social Care

Abstract: HYPOTHESIS AND AIMS: Machine learning (ML) methods to post-process whole body (WB) diffusion-weighted (DW) MRI scans in myeloma patients will improve the detection & assessment of the extent of active disease before and after treatment and reduce the radiological reading time (RT). DESIGN, POPULATION & ASSESSMENTS: Phase 1 (training): WB-MRI scans from a cohort of 160 myeloma patients (120 with active disease) from a single centre and 40 age-relevant healthy volunteers (HV) will be used to develop a ML detection tool to recognise active myeloma. Phase 2 (validation): Sensitivity and RT assessment using WB-MRI -/+ ML in 203 active myeloma, 100 inactive myeloma and 50 HV. Phase 3 (disease quantity): A ML quantification tool will be developed then tested on WB-MRI of 60 patients having scans before and after myeloma treatment, on iTIMM trial. Quantification score, RT and categorisation of response will be assessed +/- ML. Reference standard will be expert panel. INTERVENTION: No patient intervention is planned. Study intervention is the addition of ML detection and quantification tools to existing WB-MRI scans on patients with myeloma and HVs. Expert and non-expert radiology readers will review scans in mixed order such that paired reads +/- ML are available for analysis. OUTCOME MEASURES: Phase 1: Sensitivity of ML detection tool for active myeloma when compared to reference standard. Phase2: Primary: Difference in sensitivity of WB-MRI+/-ML to diagnose active myeloma on a per-patient basis, by expert readers, against the reference standard. Secondary: per-site sensitivity, RT, specificity and categorization of disease type of WB-MRI-/+ML; difference in sensitivity and specificity of WB-MRI+/-ML support using new readers. Phase 3: Primary: Agreement between experienced readers and reference standard in scoring disease burden, assessed +/- ML. Secondary: RT, agreement between readers in response category compared to reference; cost analysis. SAMPLE SIZE: Phase 1: 40 HV & 160 myeloma patients (120 with active disease). Phase 2: 50 HV & 100 patients with no active disease); 203 patients with active disease Phase 3: 60 patients, 120 WB-MRI scans (2 time points). STATISTICAL ANALYSIS AND ECONOMIC BENEFIT: Phase 1: Sensitivity based on ML cross-validation methods. Phase 2: % of patients with active disease who have positive reference standard on WBMRI-/+ML will be compared using McNemar’s test. Per patient and per site sensitivity -/+ML will be reported with 95% CI. Per patient and per site specificity -/+ML will be reported with 95% CI. RT will be compared using the most appropriate test. Phase 3: Differences from scores given by experienced readers and the reference will be described using Bland-Altman plots +/- ML. A simple economic benefit analysis will be performed based on reading times. DURATION OF THE STUDY: Total duration is 39 months. MAIN STAGES OF THE PROPOSED PROJECT WITH EXPECTED DURATIONS (MONTHS): Phase 1: (Months 0-16): Segmentation and development of ML detection tool. Phase 2: (Months 9-28): Validation of ML detection tool. Paired radiology reads of 353 WB-MRI +/- ML support. Further optimisation of the ML tool may be undertaken. Phase 3: (Months 16-39): Development and validation of ML quantification tool. Sequential WB-MRI scan from patients before and after treatment will be evaluated qualitatively and quantitatively for extent of disease +/- ML support. Complete report.

Lay abstract: BACKGROUND: Diffusion-weighted whole body magnetic resonance imaging (WB-MRI) is a technique that depicts myeloma deposits in the bone marrow. WB-MRI covers the entire body during the course of a single scan and can be used to detect sites of disease without using ionising radiation. Although WB-MRI allows for “at a glance” assessment of disease burden, it requires significant expertise to accurately identify and quantify active myeloma. The technique is time-consuming to report due to the great number of images. A further challenge is recognising whether a patient has residual disease after treatment. Machine learning (ML) is a computer technique that can be trained to automatically detect disease sites in order to support the radiologist’s interpretation. We believe this technique will help the radiologist to interpret the scan more accurately and quickly. This could allow the technique to be more widely used in the NHS. HOW THE PROPOSAL ADDRESSES THE RESEARCH PROPOSED: We propose to conduct a study in which we will develop and test ML methods that have the potential to increase accuracy and reduce reading time of WB-MRI scans in myeloma patients. We propose to develop ML tools to detect and quantify active disease before and after treatment based on WB-MRI. HOW AND WHERE THE RESEARCH WILL BE CARRIED OUT: Research will be carried out in the Royal Marsden NHS Foundation Trust, Institute of Cancer Research, London and Imperial College London. WB-MRI scans have already been acquired in myeloma patients; we also include 50 healthy volunteer scans (22 previously scanned). Work will be divided into three parts: 1) development of ML tool to detect active myeloma; 2) measure the ability of the ML tool to improve the radiologists’ interpretation of WB-MRI using a set of scans from patients with active and inactive myeloma and HV; 3) ML tool will be developed to quantify disease burden and changes between pre- and post-treatment scans in order to identify response to treatment. WHAT OUTCOMES WILL BE USED TO ASSESS THE SUCCESS OF THE RESEARCH: The main outcome measures for this study will be the improvement in the detection of active disease and disease burden and the reduction in radiology reading time. We will assess experienced specialist and non-specialist radiologists. WHAT ARE THE ETHICAL ISSUES INVOLVED IN THIS STUDY AND THE ARRANGEMENTS FOR HANDLING THESE: Ethical approval will be obtained prior to starting the study. MRI data will be anonymised. We will request permission to use anonymised data without patient consent. WHY THIS TEAM IS WELL PLACED TO CARRY OUT THE RESEARCH: Our multidisciplinary team consists of experts in myeloma WB-MRI, MRI physics, computing sciences, imaging trial design and myeloma treatment. PROVIDE A JUSTIFICATION FOR THE COSTS REQUESTED: We request research funds to cover the costs of the salaries of the researchers to organise and conduct the study, to disseminate the results to European and international conferences and to cover computing equipment and consumables necessary during the course of the study. Patient MRI scans for phases 1 & 2 have been provided by the NHS. Cost of scans for phase 3 is has been provided by the iTIMM study and NHS. Separate funding has been agreed for additional age-relevant healthy volunteer WB-MRI.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS