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Trial details imported from ClinicalTrials.gov

For full trial details, please see the original record at https://clinicaltrials.gov/study/NCT06546592




Registration number
NCT06546592
Ethics application status
Date submitted
25/07/2024
Date registered
9/08/2024

Titles & IDs
Public title
Locally Optimised Contouring With AI Technology for Radiotherapy
Scientific title
LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy
Secondary ID [1] 0 0
2024/PID01401
Universal Trial Number (UTN)
Trial acronym
LOCATOR
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Contouring 0 0
Segmentation 0 0
Radiation Therapy 0 0
Artificial Intelligence 0 0
Deep Learning 0 0
Condition category
Condition code

Intervention/exposure
Study type
Interventional
Description of intervention(s) / exposure
Treatment: Devices - AI assisted contouring

Experimental: 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.

No intervention: Manual contouring - Patients in this arm will have standard of care which is fully manual contours/segmentations generated and checked by a radiation oncologist.


Treatment: Devices: AI assisted contouring
Initial are generated automatically using software powered by artificial intelligence
Intervention code [1] 0 0
Treatment: Devices
Comparator / control treatment
Control group

Outcomes
Primary outcome [1] 0 0
Assessment of differences in Contour Quality
Timepoint [1] 0 0
18 months
Secondary outcome [1] 0 0
Assessment of quality of AI assisted contours with and without manual edits
Timepoint [1] 0 0
18 months
Secondary outcome [2] 0 0
Time Savings
Timepoint [2] 0 0
18 months
Secondary outcome [3] 0 0
To assess the differences in acute clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.
Timepoint [3] 0 0
18 months
Secondary outcome [4] 0 0
To assess the differences in late clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.
Timepoint [4] 0 0
5 years
Secondary outcome [5] 0 0
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.
Timepoint [5] 0 0
18 months
Secondary outcome [6] 0 0
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.
Timepoint [6] 0 0
5 years
Secondary outcome [7] 0 0
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.
Timepoint [7] 0 0
18 months
Secondary outcome [8] 0 0
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.
Timepoint [8] 0 0
5 years
Secondary outcome [9] 0 0
Assessment of accuracy of AI assisted contours before and after manual edits using surface dice similarity coefficient (sDSC).
Timepoint [9] 0 0
18 months
Secondary outcome [10] 0 0
Assessment of accuracy of AI assisted contours before and after manual edits using dice similarity coefficient (DSC).
Timepoint [10] 0 0
18 months
Secondary outcome [11] 0 0
Assessment of accuracy of AI assisted contours before and after manual edits using added path length (APL)
Timepoint [11] 0 0
18 months
Secondary outcome [12] 0 0
Assessment of accuracy of AI assisted contours before and after manual edits using mean slice-wise Hausdorff distance (MSHD).
Timepoint [12] 0 0
18 months
Secondary outcome [13] 0 0
Assessment of dosimetric differences in plans optimised on AI assisted contours before and after manual edits.
Timepoint [13] 0 0
18 months
Secondary outcome [14] 0 0
Assessment of accuracy in contours with an initial and retrained AI model using surface dice similarity coefficient (sDSC).
Timepoint [14] 0 0
18 months
Secondary outcome [15] 0 0
Assessment of accuracy in contours with an initial and retrained AI model using dice similarity coefficient (DSC).
Timepoint [15] 0 0
18 months
Secondary outcome [16] 0 0
Assessment of accuracy in contours between different AI systems using surface dice similarity coefficient (sDSC).
Timepoint [16] 0 0
18 months
Secondary outcome [17] 0 0
Assessment of accuracy in contours between different AI systems using dice similarity coefficient (DSC).
Timepoint [17] 0 0
18 months
Secondary outcome [18] 0 0
Assessment of quality in contours between different AI systems
Timepoint [18] 0 0
18 months
Secondary outcome [19] 0 0
Assessment of patient perception and attitudes on AI use in their care
Timepoint [19] 0 0
18 months
Secondary outcome [20] 0 0
Economic Cost Benefit Analysis
Timepoint [20] 0 0
18 months

Eligibility
Key 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
Minimum age
18 Years
Maximum age
No limit
Sex
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
* Patients under 18 years of age
* Patients unable to understand consent documents

Study design
Purpose of the study
Treatment
Allocation to intervention
Randomised controlled trial
Procedure for enrolling a subject and allocating the treatment (allocation concealment procedures)
Methods used to generate the sequence in which subjects will be randomised (sequence generation)
Masking / blinding
Blinded (masking used)
Who is / are masked / blinded?
The people receiving the treatment/s

The people assessing the outcomes
Intervention assignment
Parallel
Other design features
Phase
Not applicable
Type of endpoint/s
Statistical methods / analysis

Recruitment
Recruitment status
Not yet recruiting
Data analysis
Reason for early stopping/withdrawal
Other reasons
Date of first participant enrolment
Anticipated
31/01/2025
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
30/04/2030
Actual
Sample size
Target
444
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
NSW
Recruitment hospital [1] 0 0
Western Cancer Centre Dubbo - Dubbo
Recruitment hospital [2] 0 0
Central West Cancer Centre - Orange
Recruitment postcode(s) [1] 0 0
2830 - Dubbo
Recruitment postcode(s) [2] 0 0
2800 - Orange

Funding & Sponsors
Primary sponsor type
Other
Name
Royal North Shore Hospital
Address
Country

Ethics approval
Ethics application status

Summary
Brief summary
Trial website
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 0 0
Address 0 0
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for public queries
Name 0 0
Joseph Chan, BSc MBBS PhD FRANZCR
Address 0 0
Country 0 0
Phone 0 0
94631300
Fax 0 0
Email 0 0
joseph.chan@health.nsw.gov.au
Contact person for scientific queries

Data sharing statement
Will individual participant data (IPD) for this trial be available (including data dictionaries)?
No
No/undecided IPD sharing reason/comment


What supporting documents are/will be available?

No Supporting Document Provided



Results publications and other study-related documents

No documents have been uploaded by study researchers.


Results not provided in https://clinicaltrials.gov/study/NCT06546592