Locally Optimised Contouring With AI Technology for Radiotherapy
NCT Number: NCT06546592
Acronym: LOCATOR
Status: NOT_YET_RECRUITING
Study Type: INTERVENTIONAL
Phases: NA
Conditions: Contouring|Segmentation|Radiation Therapy|Artificial Intelligence|Deep Learning
Interventions: DEVICE: AI assisted contouring
Sponsor: Royal North Shore Hospital
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 520
Study Design: Allocation: RANDOMIZED|Intervention Model: PARALLEL|Masking: DOUBLE (PARTICIPANT, OUTCOMES_ASSESSOR)|Primary Purpose: TREATMENT
Start Date: 2024-10-01
Completion Date: 2029-06-01

Summary:
LOCATOR is a phase II randomised clinical trial that is looking at the process of contouring in radiation treatment. 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.

Primary Outcome Measures: Time Savings, To evaluate the difference in time taken to contour with and without the assistance of an auto-segmentation tool., 18 months|Contour Quality, To assess the contour quality of fully manual segmentation vs AI assisted segmentation. This assessment will be done using the MD Anderson Cancer Centre five-point likert scale used to validate autosegmentation models ranging from (Strongly disagree to Strongly Agree)., 3 years
Secondary Outcome Measures: Assessment of accuracy of AI assisted contours before and after manual edits using surface dice similarity coefficient (sDSC)., To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in surface dice similarity coefficient (sDSC)., 3 Years|Assessment of accuracy of AI assisted contours before and after manual edits using dice similarity coefficient (DSC)., To assess accuracy (geometrically) of AI segmentation before and after manual correction. This will be done by comparing the change in dice similarity coefficient (DSC)., 3 Years|Assessment of quality of AI assisted contours with and without manual edits, To assess the contour quality of AI assisted contours with and without manual edits. The contour quality assessment will be done using the MD Anderson Cancer Centre five-point likert scale used to validate autosegmentation models ranging from (Strongly disagree to Strongly Agree)., 3 Years|Assessment of dosimetric differences in plans optimised on AI assisted contours before and after manual edits., We will assess dosimetric differences to the clinical tumour volume (CTV), planning target volume (PTV) and organs at risk (OARs) between AI assisted contours before and after manual edits. Dosimetric differences will be reported as differences in dose volume histogram (DVH) constraints., 3 years|Assessment of accuracy in contours with an initial and retrained AI model using surface dice similarity coefficient (sDSC)., To assess improvements, if any, in accuracy (geometrically) on contours generated on an initial AI model versus models re-trained on clinical trial data every 50-100 patients. Comparisons will be made using the change in surface dice similarity coefficient (sDSC) when the initially generated AI contour is compared with the final edited contour., 3 years|Assessment of accuracy in contours with an initial and retrained AI model using dice similarity coefficient (DSC)., To assess improvements, if any, in accuracy (geometrically) on contours generated on an initial AI model versus models re-trained on clinical trial data every 50-100 patients. Comparisons will be made using the change in dice similarity coefficient (DSC) when the initially generated AI contour is compared with the final edited contour., 3 years|Assessment of accuracy in contours between different AI systems using surface dice similarity coefficient (sDSC)., To compare the accuracy of an in-house AI segmentation tool (LOCATOR) against commercially available tools on geometric accuracy. Comparisons will be made using the difference in surface dice similarity coefficient (sDSC) with the initially generated AI contours when compared with the final manual contour., 3 years|Assessment of accuracy in contours between different AI systems using dice similarity coefficient (DSC)., To compare the accuracy of an in-house AI segmentation tool (LOCATOR) against commercially available tools on geometric accuracy. Comparisons will be made using the difference in dice similarity coefficient (DSC) with the initially generated AI contours when compared with the final manual contour., 3 years|Assessment of quality in contours between different AI systems, To compare the quality of contours of an in-house AI segmentation tool (LOCATOR) against commercially available tools .This assessment will be done using the MD Anderson Cancer Centre five-point likert scale used to validate autosegmentation models ranging from (Strongly disagree to Strongly Agree)., 3 years|Assessment of patient perception and attitudes on AI use in their care, We will perform a brief assessment of patient perception on AI use in their care with a six question survey following their treatment on a five-point likert scale (strongly agree to strongly disagree)., 3 years|Economic Cost Benefit Analysis, To perform an economic cost-benefit analysis of using an in-house auto-segmentation (LOCATOR) tool compared to manual segmentation and commercial auto-segmentation systems. This will be done using direct dollar (US and Australian) cost comparisons. Direct costs will be calculated for the LOCATOR system including labor, hardware and maintenance costs for 1 and 3 years. The direct dollar cost for a commercial system will be compared against the overall direct cost of the LOCATOR system. The direct cost of retaining a manual system will be calculated based on the direct cost of extra hours of labor required., 3 years