Time is NTCP: Should we maximize patient throughput or perform online adaptation on proton therapy systems?

Background: Compared to conventional radiotherapy (XT), proton therapy (PT) may improve normal tissue complication probabilities (NTCP). However, PT typically requires higher adaptation rates due to an increased sensitivity to anatomical changes. Systematic online adaptation may address this issue, but it requires additional replanning time, decreasing patient throughput.

Efficient proton arc optimization and delivery through energy layer pre-selection and post-filtering.

Background: Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time.

Purpose: This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering.

Machine learning-based automatic proton therapy planning: Impact of post-processing and dose-mimicking in plan robustness.

Purpose: Automated treatment planning strategies are being widely implemented in clinical routines to reduce inter-planner variability, speed up the optimization process, and improve plan quality. This study aims to evaluate the feasibility and quality of intensity-modulated proton therapy (IMPT) plans generated with four different knowledge-based planning (KBP) pipelines fully integrated into a commercial treatment planning system (TPS).

Materials/methods: A data set containing 60 oropharyngeal cancer patients was split into 11 folds, each containing 47 patients for training, five patients for validation, and five patients for testing.

Dose mimicking based strategies for online adaptive proton therapy of head and neck cancer.

To compare a not adapted (NA) robust planning strategy with three fully automated online adaptive proton therapy (OAPT) workflows based on the same optimization method: dose mimicking (DM). The added clinical value and limitations of the OAPT methods are investigated for head and neck cancer (HNC) patients.The three OAPT strategies aimed at compensating for inter-fractional anatomical changes by mimiking different dose distributions on corrected cone beam CT images (corrCBCTs).

Evaluation of the clinical value of automatic online dose restoration for adaptive proton therapy of head and neck cancer.

Introduction: Intensity modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. This study compares not-adapted (NA) robust plans to two adaptive IMPT methods - a fully-offline adaptive (FOA) and a simplified automatic online adaptive strategy (dose restoration (DR)) to determine the benefit of DR, in head and neck cancer (HNC).

Material/methods: Robustly optimized clinical IMPT doses in planning-CTs (pCTs) were available for a cohort of 10 HNC patients.