Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy
| Autor: | Susan G.R. McDuff, Sua Yoo, Suzanne Catalano, Rachel C. Blitzblau, Jay Morrison, Colin E. Champ, Fang-Fang Yin, Q. Jackie Wu, L. O'Neill, Yang Sheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
lcsh:Medical physics. Medical radiology. Nuclear medicine
Nodal irradiation medicine.medical_treatment lcsh:R895-920 Therapy planning Machine learning computer.software_genre lcsh:RC254-282 Standard deviation 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Medicine Radiology Nuclear Medicine and imaging Scientific Article Whole breast Radiation treatment planning business.industry Breast radiation lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Radiation therapy Oncology 030220 oncology & carcinogenesis Maximum dose Artificial intelligence business computer |
| Zdroj: | Advances in Radiation Oncology, Vol 6, Iss 2, Pp 100656-(2021) Advances in Radiation Oncology |
| ISSN: | 2452-1094 |
| Popis: | Purpose: The machine learning–based automated treatment planning (MLAP) tool has been developed and evaluated for breast radiation therapy planning at our institution. We implemented MLAP for patient treatment and assessed our clinical experience for its performance. Methods and Materials: A total of 102 patients of breast or chest wall treatment plans were prospectively evaluated with institutional review board approval. A human planner executed MLAP to create an auto-plan via automation of fluence maps generation. If judged necessary, a planner further fine-tuned the fluence maps to reach a final plan. Planners recorded the time required for auto-planning and manual modification. Target (ie, breast or chest wall and nodes) coverage and dose homogeneity were compared between the auto-plan and final plan. Results: Cases without nodes (n = 71) showed negligible ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|