AI in Proton Therapy Treatment Planning: A Review.
AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Addressing challenges in validation and implementation will be essential for its translation into routine clinical practice.
Authors
- M. Fatyga
- J Daniel
- Hongying Feng (ORCID: https://orcid.org/0000-0003-2929-4922)
- Thomas J. Whitaker (ORCID: https://orcid.org/0000-0001-8950-9097)
- Samir H. Patel (ORCID: https://orcid.org/0000-0002-8251-574X)
- Charles B. Simone (ORCID: https://orcid.org/0000-0002-0867-3694)
- Nancy Y. Lee
- Jonathan B. Ashman (ORCID: https://orcid.org/0000-0001-7098-9465)
- Martin Bues (ORCID: https://orcid.org/0000-0003-3309-9396)
- Haibo Lin
- Tianming Liu (ORCID: https://orcid.org/0000-0003-2245-7231)
- Yuzhen Ding (ORCID: https://orcid.org/0000-0001-9888-6679)
- Steven J. Frank (ORCID: https://orcid.org/0000-0002-7905-7792)
- Sujay A. Vora
- Wei Liu (ORCID: https://orcid.org/0000-0003-0282-6239)
Institutions
- Mayo Clinic (US)
- Memorial Sloan Kettering Cancer Center (US)
- China Three Gorges University (CN)
- WinnMed (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- PubMed
- Published
- 2025-10-22
- Primary Topic
- Radiation Therapy and Dosimetry
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Cancer Institute