Clinical trial NCT06546592: Locally Optimised Contouring With AI Technology for Radiotherapy
Official title: LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Contouring
Segmentation
Radiation Therapy
Artificial Intelligence
Deep Learning
Keywords: Contouring
Segmentation
Radiation Therapy
Artificial Intelligence
Deep Learning
Lead sponsor: Royal North Shore Hospital
Sponsor class: OTHER
First posted: 2024-08-09
Enrollment: 444
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: TREATMENT

Brief summary: LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients. This study looks at whether contouring aided by artificial intelligence (AI) is comparable in quality to that of contouring done completely manually by a radiation oncologist. We are also looking at whether AI assisted contouring saves radiation oncologists time when compared to fully manual contouring.

LOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.

Detailed description: LOCATOR is a multicentre phase II non-inferiority randomised controlled trial looking at comparing AI assisted contours (with in-house LOCATOR software) against fully manual contouring in breast cancer patients. The primary endpoint is to show non inferiority in grade of AI assisted contouring when compared to fully manual contouring with a poor contour (score \<= 2) as per the MD Anderson Contouring Grade Scale. Secondary endpoints include geometric assessments of contour accuracy, dosimetric differences based on contours, performance (geometric) when compared to commercially available tools as well as economic cost-benefit analysis if in-house AI contouring tools.

The study will randomise patients 3:1 to the intervention arm of LOCATOR assisted contours to manual contours. An initial AI contouring model for each tumor type will be trained on contours from 45 previous breast cases using a nnUNetv2 framework. The model will then be iteratively updated every 20-50 patients.

Eligibility criteria: Inclusion Criteria:

* 18 years and older who are planned for primary breast malignancy
* ECOG performance 0-2
* Ability to understand and willingness to sign a written informed consent document
* The target volume must be able to be objectively reviewed by current published national or international clinical guidelines

Exclusion Criteria:

* Patients under 18 years of age
* Patients unable to understand consent documents

Primary outcomes:
measure: Assessment of differences in Contour Quality
timeFrame: 18 months
Secondary outcomes:
measure: Assessment of quality of AI assisted contours with and without manual edits
timeFrame: 18 months
measure: Time Savings
timeFrame: 18 months
measure: To assess the differences in acute clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 18 months
measure: To assess the differences in late clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 5 years
measure: To assess the differences in patient reported general acute quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 18 months
measure: To assess the differences in patient reported general late quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 5 years
measure: To assess the differences in patient reported breast specific acute quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 18 months
measure: To assess the differences in patient reported breast specific late quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.
timeFrame: 5 years
measure: Assessment of accuracy of AI assisted contours before and after manual edits using surface dice similarity coefficient (sDSC).
timeFrame: 18 months
measure: Assessment of accuracy of AI assisted contours before and after manual edits using dice similarity coefficient (DSC).
timeFrame: 18 months
measure: Assessment of accuracy of AI assisted contours before and after manual edits using added path length (APL)
timeFrame: 18 months
measure: Assessment of accuracy of AI assisted contours before and after manual edits using mean slice-wise Hausdorff distance (MSHD).
timeFrame: 18 months
measure: Assessment of dosimetric differences in plans optimised on AI assisted contours before and after manual edits.
timeFrame: 18 months
measure: Assessment of accuracy in contours with an initial and retrained AI model using surface dice similarity coefficient (sDSC).
timeFrame: 18 months
measure: Assessment of accuracy in contours with an initial and retrained AI model using dice similarity coefficient (DSC).
timeFrame: 18 months
measure: Assessment of accuracy in contours between different AI systems using surface dice similarity coefficient (sDSC).
timeFrame: 18 months
measure: Assessment of accuracy in contours between different AI systems using dice similarity coefficient (DSC).
timeFrame: 18 months
measure: Assessment of quality in contours between different AI systems
timeFrame: 18 months
measure: Assessment of patient perception and attitudes on AI use in their care
timeFrame: 18 months
measure: Economic Cost Benefit Analysis
timeFrame: 18 months
Other outcomes:
Locations:
city: Dubbo
country: Australia
facility: Western Cancer Centre Dubbo
state: New South Wales
city: Orange
country: Australia
facility: Central West Cancer Centre
state: New South Wales
city: St Leonards
country: Australia
facility: Department of Radiation Oncology, Royal North Shore Hospital
state: New South Wales
Study officials:
References: