Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types
Lei Yu, Qianxi Ni, Binbing Wang, Kang Zhang, Feng Shi, Shixiong Huang, Guoping Shan, Yang Zhong, Ying Guo, Zhen Zhang, Jiazhou Wang, Weigang Hu · 2025
Read Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer types by Lei Yu, Qianxi Ni, Binbing Wang, Kang Zhang, Feng Shi, Shixiong Huang, Guoping Shan, Yang Zhong, Ying Guo, Zhen Zhang, Jiazhou Wang, Weigang Hu (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12827991.txt.
Description
Lei Yu and co-authors test AI-driven automatic planning for radiotherapy across five cancer types, in a 2025 study. Deep learning can draft treatment plans fast, but fixed drafts may miss personal trade-offs. Their hybrid blends learned dose prediction with goal-guided optimisation to yield ready-to-deliver plans in five minutes.
Models trained at one hospital were tested at three, with goals tailored per case. Over eighty percent of 250 auto-plans met clinical rules, and sixty percent beat hand-made plans in blinded review. Dose measures matched or topped human plans. The authors argue the hybrid keeps speed while restoring the flexibility planners need. Work continues on wider adoption across clinics.
Catalog record
| CREATOR | Lei Yu, Qianxi Ni, Binbing Wang, Kang Zhang, Feng Shi, Shixiong Huang, Guoping Shan, Yang Zhong, Ying Guo, Zhen Zhang, Jiazhou Wang, Weigang Hu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 67 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |