Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913427092406272 |
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| author | Fischer, Paul Willms, Hannah Schneider, Moritz Thorwarth, Daniela Muehlebach, Michael Baumgartner, Christian F. |
| author_facet | Fischer, Paul Willms, Hannah Schneider, Moritz Thorwarth, Daniela Muehlebach, Michael Baumgartner, Christian F. |
| contents | Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08432 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy Fischer, Paul Willms, Hannah Schneider, Moritz Thorwarth, Daniela Muehlebach, Michael Baumgartner, Christian F. Machine Learning Cancer remains a leading cause of death, highlighting the importance of effective radiotherapy (RT). Magnetic resonance-guided linear accelerators (MR-Linacs) enable imaging during RT, allowing for inter-fraction, and perhaps even intra-fraction, adjustments of treatment plans. However, achieving this requires fast and accurate dose calculations. While Monte Carlo simulations offer accuracy, they are computationally intensive. Deep learning frameworks show promise, yet lack uncertainty quantification crucial for high-risk applications like RT. Risk-controlling prediction sets (RCPS) offer model-agnostic uncertainty quantification with mathematical guarantees. However, we show that naive application of RCPS may lead to only certain subgroups such as the image background being risk-controlled. In this work, we extend RCPS to provide prediction intervals with coverage guarantees for multiple subgroups with unknown subgroup membership at test time. We evaluate our algorithm on real clinical planing volumes from five different anatomical regions and show that our novel subgroup RCPS (SG-RCPS) algorithm leads to prediction intervals that jointly control the risk for multiple subgroups. In particular, our method controls the risk of the crucial voxels along the radiation beam significantly better than conventional RCPS. |
| title | Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.08432 |