Here is the design of a clinical trial:

Official Title: Development of a Machine Learning Support for Reading Whole Body Diffusion Weighted Magnetic Resonance Imaging (WB-DW-MRI) in Myeloma for the Detection and Quantification of the Extent of Disease Before and After Treatment

Included conditions:
- Myeloma

Study Armgroups:
- {'label': 'Phase 1 - Mixed Scan Data Training Set', 'type': 'OTHER', 'description': '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.', 'interventionNames': ['Other: Machine Learning (ML)']}
- {'label': 'Phase 2 - Mixed Scan Data Validation Set', 'type': 'OTHER', 'description': '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.', 'interventionNames': ['Other: Machine Learning (ML)']}
- {'label': 'Phase 3 - Disease Burden Paired Data Set', 'type': 'OTHER', 'description': '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.', 'interventionNames': ['Other: Machine Learning (ML)']}

Interventions:
- {'type': 'OTHER', 'name': 'Machine Learning (ML)', 'description': 'Application of ML support algorithm to accelerate and enhance human interpretation of WB-MRI scans in patients with myeloma', 'armGroupLabels': ['Phase 1 - Mixed Scan Data Training Set', 'Phase 2 - Mixed Scan Data Validation Set', 'Phase 3 - Disease Burden Paired Data Set'], 'otherNames': ['Algorithm', 'Software', 'Decision support tool', 'Convolutional neural network']}

Primary Outcomes:
- {'measure': 'Sensitivity of Machine Learning Algorithm to detect Myeloma', 'description': 'Sensitivity for the detection of active myeloma on WB-MRI with and without ML support versus the reference standard', 'timeFrame': '20 months'}

Please estimate the sample size based on the assumption: 
The study aims for 80% power with a two-sided alpha of 0.05. The estimated proportion of discordant pairs is 0.154. Specificity is assumed to be unchanged. Non-inferiority limit is set at 10%.
Sample size
        
          Phase 1
          We will train the ML algorithm on a set of scans without and with active disease that will reflect the categories of disease that may be encountered in clinical practice. The number of cases used for training are arbitrarily chosen reflecting the knowledge that a large number of training data sets will improve training accuracy, counterbalanced with the resources needed to curate and annotate a large number of data sets.
        
        
          Phase 2
          The study is powered on the primary outcome of sensitivity.
          In a meta-analysis, Wu et al have reported a pooled sensitivity of 88% and a pooled specificity of 86% (0.86 for WB-MRI with DW-MRI).8 We anticipate that the addition of ML could increase this by at least 7.5%, from 88% to 95.5%. There is no background data to indicate the expected proportion of discordant pairs, so we have estimated this as (1â0.955)Ã0.88+0.955Ã(1â0.88), which is equal to 0.154. To achieve 80% power using a two-sided alpha of 0.05 would require a total of 203 patients positive for myeloma using the gold standard.
          If it is assumed that the specificity will be unchanged using ML, a total number of cases with no active disease of 150 (50 HV, 100 inactive treated myeloma) will give 80% power to show that the difference is above a non-inferiority limit of 10%.
        
        
          Phase 3 training
          Approximately 200 cases that have at least two time points will be taken from phases 1 and 2, with active disease present at least at one time point, and used for training and validation for burden of disease; this will ensure efficient use of all data and segmentations.
        
        
          Phase 3 clinical testing
          This sample size is fixed at 60 patients, the full sample size of the iTIMM study, each of whom has a baseline and one post-treatment scan.