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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05653063
Other study ID # ARCHERY
Secondary ID 1U01CA269143-011
Status Recruiting
Phase
First received
Last updated
Start date December 7, 2023
Est. completion date September 1, 2026

Study information

Verified date May 2024
Source University College, London
Contact ARCHERY Trial Manager
Phone +442076704637
Email mrcctu.archery@ucl.ac.uk
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The aim of this study is to look at whether an Artificial Intelligence (AI) based computer program can automate two components of the radiotherapy treatment pathway to a sufficient quality standard to enable its routine clinical use. The two components include the delineation (outlining) of anatomical areas that are at risk of tumour spread and at risk of radiation damage, and the definition of the position, size and shape of the radiation beams. The AI-based computer programs have been developed to perform tasks that would normally require direct human involvement by oncologists and medical physicists. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy, which this study will test.


Recruitment information / eligibility

Status Recruiting
Enrollment 990
Est. completion date September 1, 2026
Est. primary completion date December 1, 2025
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: 1. Consecutive patients with histologically confirmed head and neck cancers of the oropharynx, larynx, hypopharynx and nasopharynx (Stage I-III) that have given consent for radical radiotherapy. 2. Consecutive histologically confirmed primary cervical cancer patients (Stage IB-IIIB including pelvic node positive) that have given consent for radical radiotherapy. 3. Consecutive histologically confirmed primary prostate cancer patients (T1-4N0M0) that have given consent for radical radiotherapy. 4. Mental capacity to understand and consent to participate in the study. 5. Patients aged =18years. Exclusion Criteria: 1. Patients requiring radiotherapy after curative surgery or surgery that is intended to remove as much of the tumour as possible. 2. Patients receiving palliative radiotherapy 3. Patients aged < 18years.

Study Design


Intervention

Other:
A web-based artificial intelligence (AI) auto-planning tool
The CT scan taken at the time of treatment planning is uploaded to a web server called the Radiotherapy planning assistant which automates the contouring of target organs and areas of high-risk disease as well as defining the size, shape and number of radiotherapy beams to treat the cancer. The final plan is downloaded to the local treatment planning system where the doses are recalculated and clinical peer review is undertaken before the plan can be used clinically. In this study patients will not be treated with the AI tool but the manual plan created by the local teams.

Locations

Country Name City State
India Tata Medical Centre Kolkata
India Tata Memorial Hospital Mumbai
Jordan King Hussein Cancer Center Amman
Malaysia University of Malaya Medical Center Kuala Lumpur
South Africa Groote Schuur Hospital Cape Town
South Africa Tygerberg Hospital Stellenbosch

Sponsors (14)

Lead Sponsor Collaborator
University College, London King Hussein Cancer Center, M.D. Anderson Cancer Center, Medical Research Council, Mount Vernon Cancer Centre at Mount Vernon Hospital, National Cancer Institute (NCI), National Institutes of Health (NIH), Rising Tide Foundation, Tata Memorial Centre, Tata Memorial Hospital, University Ghent, University of Cape Town, University of Malaya, University of Stellenbosch

Countries where clinical trial is conducted

India,  Jordan,  Malaysia,  South Africa, 

Outcome

Type Measure Description Time frame Safety issue
Primary Proportion of radiotherapy treatment plans that have contours and dosimetry that meet pre-defined criteria for clinical acceptability Prior to first treatment
Secondary comparison of time and human resource requirements between producing automated radiotherapy treatment plans using artificial intelligence and producing treatment plans using the standard manual pathway radiotherapy plan preparation process pre treatment
Secondary Comparison of radiotherapy treatment costs using the artificial intelligence automated pathway and standard manual planning pathway radiotherapy treatment plan preparation process and treatment interval
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