Accelerated Body Diffusion-Weighted MRI Using Artificial Intelligence

Cancer Clinical Trial

— CeleScan-R  

Official title:

Accelerated Body Diffusion-Weighted MRI Using Artificial Intelligence

Clinical Trial Details — Status: Enrolling by invitation

Administrative data

• NCT number: NCT05380609
• Other study ID #: CCR5653
• Status: Enrolling by invitation
• Start date: May 6, 2022
• Est. completion date: April 2025

Study information

• Verified date: May 2022
• Source: Royal Marsden NHS Foundation Trust
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Whole-body diffusion-weighted MRI (WBDWI) is a non-invasive tool used for staging and response evaluation in oncologic practice and is at the core of emerging response criteria in advanced prostate and breast cancers.

WBDWI is a sensitive tool that radiologists can use to review the extent of disease and is achieved using a series of sequential imaging stations from the head to the mid-thigh. WBDWI accounts for more than 50% of the acquisition time of conventional whole-body MRI studies with a 1-hour duration. Despite national and international guidance for using whole-body MRI, a recent UK survey indicated that only 27% of UK radiology departments were offering a whole-body MRI service with a lack of scanner availability cited by 50% of respondents as the main challenge to service delivery. In the context of the ever-increasing capacity pressures on MRI departments, reducing acquisition times would facilitate the wider adoption of clinical WBDWI, reduce costs, and improve the patient experience.

DWI is also embedded into consensus MRI protocols across almost all tumour types including primary prostate and breast cancers, metastatic liver disease, gynaecological cancers & GI cancers, where acquisition time savings could also be beneficial. The investigators have previously published accelerated DWI with deep learning based denoising filters (quickDWI), which can provide up to 50% reduction in whole-body MRI acquisition times. The goal of the deep-learning algorithm is to remove the noise in these subsampled images, producing an image with acceptable clinical quality.

The aim of this investigation is to extend this work by testing quickDWI within a larger retrospective data cohort, incorporating other cancers such as disease of the abdomen and pelvis, primary prostate cancer, liver metastases, and pancreatic cancer.

Recruitment information / eligibility

• Status: Enrolling by invitation
• Enrollment: 450
• Est. completion date: April 2025
• Est. primary completion date: April 2025
• Gender: All
• Age group: 18 Years and older

Eligibility

1. Patients who have received one of the following MRI examinations (scan type shown in parentheses):

• Multiple Myeloma (whole-body)

• Metastatic Prostate Cancer (whole-body)

• Metastatic Breast Cancer (whole-body)

• Liver Metastases (stacked abdomen/pelvis)

• Pancreatic Cancer (stacked abdomen/pelvis)

• Gynaecological Cancers (stacked abdomen/pelvis)

• Gastrointestinal Cancers (stacked abdomen/pelvis)

• Primary Prostate Cancers (multiparametric Prostate exam)

2. Patients who have received one of the above-mentioned scan types using a modified clinical multi-direction diffusion-weighted (MDDW) MRI protocol as part of standard of care.

Exclusion Criteria:

• No exclusion criteria as this is a retrospective data study only.

Related Conditions & MeSH terms

• Body Weight
• Cancer

Locations

Country	Name	City	State
United Kingdom	Department of Radiology, The Royal Marsden NHS Foundation Trust	Sutton	Surrey

Sponsors (1)

Lead Sponsor	Collaborator
Royal Marsden NHS Foundation Trust

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Qualitative image comparison	The primary endpoint is the qualitative comparison of radiological image quality on a 5-point Likert scale (5 being the best) for quickDWI images and conventional clinical images.	Throughout study completion, 3 years
Secondary	Qualitative contrast-to-noise-ratio comparison	Qualitative radiological contrast-to-noise-ratio (CNR) on a 5-point Likert scale (5 being the best), for quickDWI images and conventional clinical images.	Throughout study completion, 3 years
Secondary	Qualitative artefact comparison	Qualitative scoring for presence of image artefacts on a 5-point Likert scale (5 being the best), for quickDWI images and conventional clinical images.	Throughout study completion, 3 years
Secondary	Inter-observer comparison	Krippendorff's alpha coefficient for inter-observer agreement of image quality, CNR and image artefacts.	Throughout study completion, 3 years
Secondary	Repeatability comparison	The coefficient of repeatability of median ADC measurements within regions-of-interest (ROIs) defined in the same anatomical areas on both quickDWI and standard clinical imaging.	Throughout study completion, 3 years