This study is investigating how AI can help doctors outline the prostate on an ultrasound image to make a custom radiation plan during a specialized type of radiation treatment for prostate cancer called brachytherapy.
This is a Phase II prospective study evaluating the standard U-net, a deep learning AI algorithm for auto-contouring of the prostate during HDR prostate brachytherapy with the needles in place by new learners. Contouring will be done on TRUS. The study will be conducted with a randomized design. Each patient will be assigned to a new learner and then randomized to manual versus AI-assisted contouring. The randomization will be stratified by new learner type: resident versus fellow/new attending. The hypothesis is that AI-assisted learner contours will have improved Dice coefficients with respect to clinically approved contours compared with manual learner contours. All brachytherapy contours will undergo review by the treating radiation oncologist who is the experienced clinician for clinical approval prior to patient treatment. The experienced clinician will be blinded to the randomization.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
SINGLE
Enrollment
36
Utilizing the addition of artificial intelligence during prostate contouring.
Brigham and Women's Hospital
Boston, Massachusetts, United States
Clinically approved contour vs. Manual and AI assisted contours
Dice coefficient \[0: no match, 1: complete match\] between the final clinically approved brachytherapy prostate contours versus the manual and AI-assisted contours provided by the new learner.
Time frame: 1 day
Contouring time
Contouring time-to-completion needed to generate and edit manual vs AI-assisted contours for a new learner.
Time frame: 1 day
Impression of AI or manual contours by new learner
Subjective impressions/perception of AI or manual contours by the new learner based on survey responses.
Time frame: 1 day
Impression of AI or manual contours by experienced clinician
Subjective impressions/perception of AI or manual contours by the experienced clinician based on survey responses.
Time frame: 1 day
Clinician contours vs. Trus images with and without needles
Dice coefficients of expert clinician contours between TRUS images with implanted needles and TRUS images after removal of bottom needle rows.
Time frame: 1 day
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