Background In recent years, deep‐learning models have been used to predict entire three‐
dimensional dose distributions. However, the usability of dose predictions to improve plan …

Abstract Background In the Danish Head and Neck Cancer Group (DAHANCA) 35 trial,
patients are selected for proton treatment based on simulated reductions of Normal Tissue …

Background and purpose Adaptive radiotherapy (ART) relies on re-planning to correct
treatment variations, but the optimal timing of re-planning to account for dose changes in …

Purpose Radiation treatment planning is highly complex and can have significant inter-and
intra-planner inconsistency, as well as variability in planning time and plan quality …

Background and purpose Automatic review of breast plan quality for clinical trials is time-
consuming and has some unique challenges due to the lack of target contours for some …

Background The framework of adaptive radiation therapy (ART) was crafted to address the
underlying sources of intra‐patient variation that were observed throughout numerous …

Purpose Partial stereotactic ablative boost radiotherapy (P‐SABR) effectively treats bulky
lung cancer; however, the planning process for P‐SABR requires repeated dose …

Background and purpose Studies investigating the application of Artificial Intelligence (AI) in
the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this …

Background and purpose We proposed an artificial neural network model to predict
radiobiological parameters for the head and neck squamous cell carcinoma patients treated …

Purpose Knowledge‐based planning (KBP) offers the ability to predict dose‐volume metrics
based on information extracted from previous plans, reducing plan variability and improving …