Title: Development and evaluation of machine learning methods in whole body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer. (MAchine Learning In whole Body Oncology, MALIBO)
Funder: Department of Health and Social Care

Abstract: HYPOTHESIS AND AIMS: In patients with cancer being staged with whole body diffusion-weighted MR (WB-DW-MR), machine learning (ML) methods to automatically detect metastatic lesions may improve diagnostic accuracy (DA) and reduce radiology reading time (RT). We aim to test ML methods in WB-DW-MR scans in patients with known cancer and established disease stage. DESIGN, POPULATION, ASSESSMENTS: Phase 1: Development of ML pipeline ‘A’ for automatic anatomic labeling in WB-DW-MR of 50 healthy volunteers using segmentation techniques. Phase 2 training: 150 scans from NIHR STREAMLINE (colorectal/lung cancer, CRUK MELT (lymphoma)& MASTER (lymphoma/prostate cancer) main studies with established disease stage will be used to train ML detection of metastases. Double radiology reads +ML ‘A’ support will use main study reference standards to identify true lesions and ML errors in order to refine ML algorithm through ‘A+’, ‘B’ and finally ‘C’. Interim sensitivity tested in 40-50 scans. Phase 3 validation: 217 scans from the primary studies will be read by radiologists with +ML ‘C’ using sequential viewing of sequences; internal pilot in first 50-70. DA will be measured against the main study reference standards and RT +/- ML will be recorded. INTERVENTIONS: WB scans in will be read +ML ‘C’ support by two expert and one new WB-MR reader, with additional sub-studies in inter-observer variation and RT. ML will automatically identify suspicious areas for review by radiologist. PRIMARY OUTCOME MEASURE: per-patient specificity of WB-DW-MR +ML ‘C’ compared to standard radiology read (WB-DW-MR from main study) against the reference standard established in the main study. SECONDARY OUTCOME MEASURES: comparison of +ML ‘C’ results with –ML results from main studies: 1. Sensitivity of WB-MR; 2. Read time (RT) 3. inter-reader variability; 4. DA of inexperienced readers; 5. different combinations of WB-MR sequences; 6. simple cost-effectiveness analysis measured by the cost of applying ML support against the time and resource (number of possible additional staging tests) saved. SAMPLE SIZE: Phase 2: 150 patients. 40-50 additional patients for interim sensitivity analysis. Phase 3: 217 patient from STREAMLINE, MELT and MASTER studies. STASTISTICAL ANALYSIS: PRIMARY: The per patient specificities of the two methods against reference standard will be compared using McNemar’s test for paired proportions. SECONDARY: to include: sensitivity per patient and per lesion, specificity per lesion; RT; inter-observer variance; diagnostic accuracy of different MR sequences. MAIN STAGES OF PROJECT & EXPECTED DURATION: Phase 1 (months 0-9): Healthy volunteer data for development of ML pipeline ‘A’. Phase 2 training (months 10-29): 150 scans read by two experts to identify true lesions and ML errors; optimisation of ML algorithm to A+, B and final ML ‘C’. Interim analysis of sensitivity in 40-50 scans. Phase 3 validation (months 28-42): Reading of 217 scans +ML’C’ by two expert and one non expert WB reader. Comparison of DA and RT from main studies (- ML). Complete final report.

Lay abstract: BACKGROUND: Whole body magnetic resonance with diffusion weighted imaging (WB-DW-MR) is a technique that shows tumour sites in many different parts of the body, based on the appearance of different tissue types and the water motion within tumour tissue when compared to healthy tissue. There is no burden of ionising radiation. Although WB-DW-MR allows for “at a glance” assessment of disease extent, it is subject to some error, for example, some normal structures may resemble the appearance of tumour. Also, WB-MR is time-consuming to report by the radiologist due to the large number of images. HOW THE PROPOSAL ADDRESSES THE RESEARCH PROPOSED: We propose to conduct a study during which we will develop and evaluate an innovative machine learning (ML) method to support radiology reporting by automatically detecting lesions and potentially increasing the diagnostic accuracy (DA) and reducing the reading time (RT) of WB-DW-MRI scans in patients with cancer. HOW AND WHERE THE RESEARCH WILL BE CARRIED OUT: Research will be carried out at Imperial College London in collaboration with the teams of main studies (NIHR STREAMLINE (colon & lung cancer patients) and CRUK MELT (lymphoma patients)) who have recruited all the patients for WB-DW-MR datasets. The proposed study has three stages: firstly, WB-DW-MR from 50 healthy volunteers will be used to develop the ML method for automatic recognition of normal appearances. Secondly, the ML method will be tested on 150 WB-MR scans from the main studies, in whom the different sites of disease have already been confirmed. The ML method will be refined by radiologists who will identify the correct sites of disease and find ML errors. This will be used to improve the ML method. Thirdly, the refined ML method will be tested in a 2nd group of 169 patients from the main studies to see if the technique can improve radiology reporting by improving DA and RT. WHAT OUTCOMES WILL BE USED TO ASSESS THE SUCCESS OF THE RESEARCH: The primary outcome measure will be correct identification of patients with or without metastatic disease. We will also assess the reduction in RT, the variability between the same and different readers and between different combinations of acquired MRI data. An estimate of the possible reduction of additional patient staging tests needed due to the use of ML will be made. WHAT ARE THE ETHICAL ISSUES INVOLVED IN THIS STUDY: Ethical approval for the normal volunteer study is in place, as part of development of WB-MR at Imperial College. Ethical approval has been obtained for the main studies, which are now recruiting. We will seek ethical approval for use of the anonymised WB-MR datasets for ML evaluation; there will be no change to patient management. WHY THIS TEAM IS WELL PLACED TO CARRY OUT THE RESEARCH: Our team has experts in clinical cancer imaging, MRI physics and all have run imaging based trials. D Rueckert is internationally recognized image computing expert with ML expertise. PROVIDE A JUSTIFICATION FOR THE COSTS REQUESTED (including NHS costs): The costs of all volunteer and patient WB-MR scans have been previously funded, making this a highly cost-effective study. Grant funding will cover the costs for the time of the applicants and researchers that will organise and conduct the study and analyse the results. Also included are expenses for equipment and consumables, trial management and dissemination of results at European and International conferencesr

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