Automating High Quality RT Planning at Scale
Journal:
arXiv
Published Date:
Jan 21, 2025
Abstract
Radiotherapy (RT) planning is complex, subjective, and time-intensive.
Advances with artificial intelligence (AI) promise to improve its precision and
efficiency, but progress is often limited by the scarcity of large,
standardized datasets. To address this, we introduce the Automated Iterative RT
Planning (AIRTP) system, a scalable solution for generating high-quality
treatment plans. This scalable solution is designed to generate substantial
volumes of consistently high-quality treatment plans, overcoming a key obstacle
in the advancement of AI-driven RT planning. Our AIRTP pipeline adheres to
clinical guidelines and automates essential steps, including organ-at-risk
(OAR) contouring, helper structure creation, beam setup, optimization, and plan
quality improvement, using AI integrated with RT planning software like Varian
Eclipse. Furthermore, a novel approach for determining optimization parameters
to reproduce 3D dose distributions, i.e. a method to convert dose predictions
to deliverable treatment plans constrained by machine limitations is proposed.
A comparative analysis of plan quality reveals that our automated pipeline
produces treatment plans of quality comparable to those generated manually,
which traditionally require several hours of labor per plan. Committed to
public research, the first data release of our AIRTP pipeline includes nine
cohorts covering head-and-neck and lung cancer sites to support an AAPM 2025
challenge. To our best knowledge, this dataset features more than 10 times
number of plans compared to the largest existing well-curated public dataset.
Repo: https://github.com/RiqiangGao/GDP-HMM_AAPMChallenge.
