This paper surveys the data‐driven dose prediction methods investigated for knowledge‐
based planning (KBP) in the last decade. These methods were classified into two major …

Treatment planning in radiation therapy has progressed enormously over the past several
decades. Such advancements came in the form of innovative hardware and algorithms …

With the advancement of treatment modalities in radiation therapy for cancer patients,
outcomes have improved, but at the cost of increased treatment plan complexity and …

The treatment planning process for patients with head and neck (H&N) cancer is regarded
as one of the most complicated due to large target volume, multiple prescription dose levels …

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 …

Purpose: To demonstrate knowledge‐based 3D dose prediction for external beam
radiotherapy. Methods: Using previously treated plans as training data, an artificial neural …

Purpose To develop a deep learning method for prediction of three‐dimensional (3D) voxel‐
by‐voxel dose distributions of helical tomotherapy (HT). Methods Using previously treated …

Purpose This work aims to study the generalizability of a pre-developed deep learning (DL)
dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and …

Purpose: The objective of this work was to develop a comprehensive knowledge‐based
methodology for predicting achievable dose–volume histograms (DVHs) and highly precise …

Purpose We propose a novel domain‐specific loss, which is a differentiable loss function
based on the dose‐volume histogram (DVH), and combine it with an adversarial loss for the …