Subgroup-Specific Risk-Controlled Dose Estimation in Radiotherapy
This work addresses the need for reliable uncertainty quantification in high-risk medical applications like radiotherapy, though it is incremental as it extends existing risk-controlling prediction sets to handle multiple subgroups.
The paper tackled the problem of providing uncertainty quantification for dose calculations in radiotherapy, where naive risk-controlling prediction sets fail to guarantee coverage for critical subgroups like radiation beam voxels, and the result was a novel subgroup-specific algorithm that significantly improves risk control for these crucial voxels compared to conventional methods.
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.