Radiation outcomes prediction (ROP) plays an important role in personalized prescription
and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation …

Artificial intelligence (AI) is beginning to transform IMRT treatment planning for head and
neck patients. However, the complexity and novelty of AI algorithms make them susceptible …

The goal of this study is to demonstrate the feasibility of a novel fully-convolutional
volumetric dose prediction neural network (DoseNet) and test its performance on a cohort of …

Deep learning algorithms have recently been developed that utilize patient anatomy and
raw imaging information to predict radiation dose, as a means to increase treatment …

Purpose To suggest an attention-aware, cycle-consistent generative adversarial network (A-
CycleGAN) enhanced with variational autoencoding (VAE) as a superior alternative to …

Aims This review paper intends to summarize the application of machine learning to
radiotherapy outcome modeling based on structured and un‐structured radiation oncology …

Quality assurance (QA) is important for ensuring precision in radiation therapy. The
complexity and resource-intensive nature of QA has increased with the continual evolution …

Purpose Establishing the tolerance limits of patient‐specific quality assurance (PSQA)
processes based on the gamma passing rate (GPR) by using normal statistical process …

Dosimetry check (DC) is a commercial software that allows reconstruction of 3D dose
distributions using transit electronic portal imaging device (EPID) images. In this work, we …

Purpose To assess the effect of sampling variability on the performance of individual charts
(I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes …