Machine Learning in Myeloma Response (MALIMAR)
Myeloma
About this trial
This is an interventional diagnostic trial for Myeloma focused on measuring Whole Body Diffusion Weighted, Magnetic Resonance Imaging, Machine Learning, Reading Time, Convolutional Neural Network, Algorithm, Diagnostic performance
Eligibility Criteria
Inclusion Criteria (healthy volunteers):
- Able to provide written informed consent
- No contra-indication to MRI
- 40 years or above in age (age matched as far as possible to WB-MRI scan set)
- No known significant illness
- No known metallic implant
Exclusion Criteria:
- Not able to provide written informed consent
- A contra-indication to MRI
- <40 years or above in age (age matched as far as possible to WB-MRI scan set)
- A known significant illness
- A known metallic implant
Sites / Locations
- Department of Radiology, The Royal Marsden NHS Foundation Trust
- Institute of Cancer Research, London
- Imperial College, London
Arms of the Study
Arm 1
Arm 2
Arm 3
Other
Other
Other
Phase 1 - Mixed Scan Data Training Set
Phase 2 - Mixed Scan Data Validation Set
Phase 3 - Disease Burden Paired Data Set
Machine learning (ML): A mixed data set of 200 WB-MRI scans comprising scans obtained from 40 healthy volunteers (scanned for the purposes of the study), 40 previously acquired inactive myeloma WB-MRI scans and 120 previously acquired active myeloma WB-MRI scans, in which machine learning and convolutional neural networks will be trained to recognise healthy marrow, treated inactive previous myeloma and active myeloma. An algorithm will be developed for testing in phase 2.
Machine Learning (ML): A mixed data set of 353 WB-MRI scans as that comprising 50 healthy volunteers (scanned for the purposes of the study), and previously acquired scans from 303 myeloma patients, 100 of whom have inactive disease and 203 of whom have active myeloma. The scans will be read by radiologists in random order either with or without the support of for the detection of active myeloma. The diagnostic performance of the radiology reads with or without the machine learning support will be measured against an expert panel reference standard.
Machine Learning (ML): Approximately 200 paired WB-MRI scans from 100 patients (scanned at baseline with active disease and then post treatment) will be used to develop a machine learning tool to quantify the burden of disease. The machine learning algorithm will then be tested on a further additional set of 60 patients who previously had two WB-MRI scans comprising paired baseline (with active disease) and post treatment scans. The agreement of radiology readers to evaluate the burden of disease will be measured against the reference standard (expert panel) with and without machine learning support.