MAchine Learning in whole Body Oncology

Development and evaluation of machine learning methods in whole body MR with diffusion weighted imaging for staging of patients with cancer

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN23068310

Enrollment

217

Registered

2015-08-28

Start date

2015-10-01

Completion date

Unknown

Last updated

2025-09-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cancer Cancer

Interventions

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 recr

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)

Exclusion criteria

Exclusion criteria: As per each source (contributing) study (http://www.isrctn.com/ISRCTN50436483, http://www.isrctn.com/ISRCTN43958015, https://clinicaltrials.gov/ct2/show/NCT01459224)

Design outcomes

Primary

Measure	Time frame
Per site sensitivity and specificity of MRI for nodal and extra-nodal sites and concordance in final disease stage with the multi-modality reference standard (at staging). The reference standard for the MELT study is contemporaneous MDT with all other staging eg PET CT and CT at the time of diagnosis and initial staging,	—

Secondary

Measure	Time frame
1. Inter-observer agreement for MR radiologists 2. Evaluation of different MRI sequences on diagnostic accuracy 3. Simulated effect of MRI on clinical management	—

Countries

England, United Kingdom

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 5, 2026