The 2nd UPLIFT School for “Artificial Intelligence in Radiotherapy”

Session

Session D

7 Nov 2026, 09:30

Congressi Stefano Franscini (CSF)

90. Real-Time Range Prediction for a Mixed Helium–Carbon Beam

Maximilian Dick (GSI Helmholtz Center for Heavy Ion Research GmbH)

07/11/2026, 09:30

oral/poster

Objective

Mixed helium-carbon beams enable online range monitoring and imaging. Both ions share the same mass-to-charge ratio, allowing simultaneous acceleration, while helium has approximately three times the range in water. However, anatomical changes beyond the carbon Bragg peak only affect the helium range. We present a deep learning approach for spot-by-spot carbon range prediction...

93. Deep-learning assisted single-scenario robust optimization for carbon ion therapy

Lennart Volz (GSI Helmholtz Center for Heavy Ion Research GmbH)

07/11/2026, 09:50

oral/poster

Objective: Robust optimization in particle therapy conventionally uses a voxel wise-worst case approach, where the plan is optimized to meet the clinical criteria even under the worst of a set of uncertainty scenarios. This entails significant computational overhead, most of which is only needed to compute the worst-case scenario. We hypothesize that optimizing only on the nominal case and a...

69. A real association is not a usable predictor: a five-stage workflow for machine learning on routine radiotherapy QA data

Alejandro del Pozo Domínguez

07/11/2026, 10:10

oral/poster

Objective
Radiotherapy services routinely generate machine and dosimetric QA data, rarely analysed together. We propose a reproducible five-stage workflow for developing machine-learning models from such data, focusing on the step least often addressed: establishing whether exploitable signal exists, and distinguishing a genuine but weak physical association from useful predictive...