Locally Optimised Contouring With AI Technology for Radiotherapy

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

444 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-02-11

Study Completion Date

2030-04-30

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Contouring Segmentation Radiation Therapy Artificial Intelligence Deep Learning

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

TREATMENT

Blinding Strategy

DOUBLE

Participants Outcome Assessors

Study Groups

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AI assisted contouring

Patients in this arm will have their contours/segmentations generated by a combination of the LOCATOR (AI) software before manual edits and checks by a radiation oncologist.

Group Type EXPERIMENTAL

AI assisted contouring

Intervention Type DEVICE

Initial are generated automatically using software powered by artificial intelligence

Manual contouring

Patients in this arm will have standard of care which is fully manual contours/segmentations generated and checked by a radiation oncologist.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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AI assisted contouring

Initial are generated automatically using software powered by artificial intelligence

Intervention Type DEVICE

Other Intervention Names

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autocontouring autosegmentation AI contouring

Eligibility Criteria

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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