Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability

no code implementations • 2 Dec 2023 • Lian Zhang, Jason M. Holmes, Zhengliang Liu, Hongying Feng, Terence T. Sio, Carlos E. Vargas, Sameer R. Keole, Kristin Stützer, Sheng Li, Tianming Liu, Jiajian Shen, William W. Wong, Sujay A. Vora, Wei Liu

The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89. 32%$\pm$1. 45% vs. 93. 48%$\pm$1. 51% vs. 96. 79%$\pm$0. 83%, OARs: 85. 87%$\pm$1. 73% vs. 91. 15%$\pm$1. 13% vs. 94. 29%$\pm$1. 01%).

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report

no code implementations • 5 Oct 2023 • Jason Holmes, Lian Zhang, Yuzhen Ding, Hongying Feng, Zhengliang Liu, Tianming Liu, William W. Wong, Sujay A. Vora, Jonathan B. Ashman, Wei Liu

Conclusions: Given the accuracy of GPT-4 in re-labeling structure names of both target volumes and normal tissues as presented in this work, LLMs are poised to be the preferred method for standardizing structure names in radiation oncology, especially considering the rapid advancements in LLM capabilities that are likely to continue.

Benchmarking