Automating RT Planning at Scale: High Quality Data For AI Training
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866909902808547328 |
|---|---|
| author | Gao, Riqiang Diallo, Mamadou Liu, Han Magliari, Anthony Sackett, Jonathan Verbakel, Wilko Meyers, Sandra Mcbeth, Rafe Zarepisheh, Masoud Arberet, Simon Kraus, Martin Ghesu, Florin C. Kamen, Ali |
| author_facet | Gao, Riqiang Diallo, Mamadou Liu, Han Magliari, Anthony Sackett, Jonathan Verbakel, Wilko Meyers, Sandra Mcbeth, Rafe Zarepisheh, Masoud Arberet, Simon Kraus, Martin Ghesu, Florin C. Kamen, Ali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11803 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automating RT Planning at Scale: High Quality Data For AI Training Gao, Riqiang Diallo, Mamadou Liu, Han Magliari, Anthony Sackett, Jonathan Verbakel, Wilko Meyers, Sandra Mcbeth, Rafe Zarepisheh, Masoud Arberet, Simon Kraus, Martin Ghesu, Florin C. Kamen, Ali Human-Computer Interaction Machine Learning Robotics 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. |
| title | Automating RT Planning at Scale: High Quality Data For AI Training |
| topic | Human-Computer Interaction Machine Learning Robotics |
| url | https://arxiv.org/abs/2501.11803 |