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

arXiv: https://arxiv.org/abs/2312.00975 · HTML: https://mlsanity.com/paper/2312.00975/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 57 references that are in the map.
- First posted on arXiv 2 Dec 2023.

## Builds on (most cited first, 5 of 5)

- [Loss Functions and Metrics in Deep Learning](https://mlsanity.com/paper/2307.02694/) (2023)
- [Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture](https://mlsanity.com/paper/1805.10397/) (2018)
- [Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations](https://mlsanity.com/paper/1812.06934/) (2018)
- [Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer](https://mlsanity.com/paper/2304.11135/) (2023)
- [Deep Dose Plugin Towards Real-time Monte Carlo Dose Calculation Through a Deep Learning based Denoising Algorithm](https://mlsanity.com/paper/2011.14959/) (2020)

## Built on by (most cited first, 1 of 1)

- [RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway](https://mlsanity.com/paper/2610.06923/) (2026)

## Abstract (arXiv)

Purpose: Prior AI-based dose prediction studies in photon and proton therapy often neglect underlying physics, limiting their generalizability to handle outlier clinical cases, especially for pencil beam scanning proton therapy (PBSPT). Our aim is to design a physics-aware and generalizable AI-based PBSPT dose prediction method that has the underlying physics considered to achieve high generalizability to properly handle the outlier clinical cases. Methods and Materials: This study analyzed PBSPT plans of 103 prostate and 78 lung cancer patients from our institution,with each case comprising CT images, structure sets, and plan doses from our Monte-Carlo dose engine (serving as the ground truth). Three methods were evaluated in the ablation study: the ROI-based method, the beam mask and sliding window method, and the noisy probing dose method. Twelve cases with uncommon beam angles or prescription doses tested the methods' generalizability to rare treatment planning scenarios. Performance evaluation used DVH indices, 3D Gamma passing rates (3%/2mm/10%), and dice coefficients for dose agreement. Results: The noisy probing dose method showed improved agreement of DVH indices, 3D Gamma passing rates, and dice coefficients compared to the conventional methods for the testing cases. 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%±1.45% vs. 93.48%±1.51% vs. 96.79%±0.83%, OARs: 85.87%±1.73% vs. 91.15%±1.13% vs. 94.29%±1.01%). The dose predictions were completed within 0.3 seconds. Conclusions: We've devised a novel noisy probing dose method for PBSPT dose prediction in prostate and lung cancer patients. With more physics included, it enhances the generalizability of dose prediction in handling outlier clinical cases.