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Updated: May 13, 2026
Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
Published on: April 11, 2018
Rapid Artificial Intelligence Autoplanning Rivals Manual Expert Planning for Cervical Brachytherapy
Aranyo Mitra1, Lance C Moore1, Karoline Kallis2
1Department of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, California.
Purpose:
The study aims to evaluate the quality and clinical acceptability of artificial intelligence automated plans compared with manual clinical plans through blinded physician review, for cervical brachytherapy applicators.
Methods And Materials:
Automated plans were generated using dose predictions from a U-Net with anatomic masks, dwell position location masks, and applicator-specific 3-dimensional dose inputs (where dose was computed using uniform dwell times). Model data included 2005 brachytherapy plans from 7 implant types (train/validation/test split = 62%/19%/19%). Test set dose predictions were fed into an optimizer to produce automated plans. Randomized automated and clinical plan pairs were presented to 10 expert gynecologic brachytherapy physicians, who indicated plan preference, scored plans from 1 to 5 (where 5 indicates the highest quality), and guessed which plan was automated. Five physicians from our center reviewed 130 plans in total across all 7 implants. Five external physicians from 3 other centers each reviewed 2 plan sets per implant type (70 plans). Autoplan scores were compared between physician groups and with clinical plans using Wilcoxon signed-rank tests (P < .05 considered significant).
Results:
Autoplans were deemed better or equivalent in approximately 50% of cases for both physician groups, with the highest preference rates for hybrid implants (>58% on average). Selection rates varied between physicians, often due to different prioritization of tumor coverage versus organ sparing and/or loading preferences. Automated and clinical plans scored 4 (acceptable plan with clinically unimportant stylistic differences) on average (P > .05 for all comparisons). Slightly reduced preference rates and scores for external physicians were attributed to stylistic planning differences not captured in model training data from our center. Physicians correctly identified about 50% of autoplans, consistent with random chance, indicating indistinguishability from clinical plans.
Conclusions:
Our brachytherapy artificial intelligence automated planning technology produced automated plans comparable in quality and indistinguishable from manual, clinical plans in a median of 1.4 minutes.
