Summary of Subgroup-specific Risk-controlled Dose Estimation in Radiotherapy, by Paul Fischer et al.
Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
by Paul Fischer, Hannah Willms, Moritz Schneider, Daniela Thorwarth, Michael Muehlebach, Christian F. Baumgartner
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach in magnetic resonance-guided linear accelerators (MR-Linacs) aims to improve radiotherapy treatment plans by incorporating deep learning frameworks for fast and accurate dose calculations. The traditional Monte Carlo simulations provide accuracy, but are computationally intensive. This paper proposes a novel method called Risk-controlling prediction sets (RCPS) that offers model-agnostic uncertainty quantification with mathematical guarantees. However, the authors find that naive application of RCPS may not be sufficient to control the risk for multiple subgroups. To address this issue, they introduce a new algorithm called Subgroup RCPS (SG-RCPS), which provides prediction intervals with coverage guarantees for unknown subgroup membership at test time. The proposed method is evaluated on real clinical planning volumes from five different anatomical regions and shows improved performance in controlling the risk of crucial voxels along the radiation beam. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cancer is a big problem, and doctors need better ways to treat it using something called radiotherapy (RT). A new tool called MR-Linacs helps doctors adjust treatment plans during RT. But for this to work well, they need fast and accurate calculations of how much radiation will be delivered. Right now, there are two main ways to do these calculations: one is very good but takes a long time, while the other is faster but not as reliable. This paper introduces a new way to calculate radiation delivery that’s both fast and reliable, which can help doctors give better treatment to patients. |
Keywords
* Artificial intelligence * Deep learning
