A review on application of deep learning algorithms in external beam radiotherapy automated treatment planning

M Wang, Q Zhang, S Lam, J Cai, R Yang - Frontiers in oncology, 2020‏ - frontiersin.org
Treatment planning plays an important role in the process of radiotherapy (RT). The quality
of the treatment plan directly and significantly affects patient treatment outcomes. In the past …

Knowledge‐based radiation treatment planning: a data‐driven method survey

S Momin, Y Fu, Y Lei, J Roper… - Journal of applied …, 2021‏ - Wiley Online Library
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 …

[HTML][HTML] A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy

C Hurkmans, JE Bibault, KK Brock, W van Elmpt… - Radiotherapy and …, 2024‏ - Elsevier
Abstract Background and purpose Artificial Intelligence (AI) models in radiation therapy are
being developed with increasing pace. Despite this, the radiation therapy community has not …

Online adaptive planning methods for intensity-modulated radiotherapy

Z Qiu, S Olberg, D den Hertog, A Ajdari… - Physics in Medicine …, 2023‏ - iopscience.iop.org
Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current
anatomy to account for inter-fraction variations before daily treatment delivery. As this …

Deep learning dose prediction for IMRT of esophageal cancer: the effect of data quality and quantity on model performance

AM Barragán-Montero, M Thomas, G Defraene… - Physica Medica, 2021‏ - Elsevier
Purpose To investigate the effect of data quality and quantity on the performance of deep
learning (DL) models, for dose prediction of intensity-modulated radiotherapy (IMRT) of …

Artificial intelligence in radiation therapy

Y Fu, H Zhang, ED Morris… - IEEE transactions on …, 2021‏ - ieeexplore.ieee.org
Artificial intelligence (AI) has great potential to transform the clinical workflow of
radiotherapy. Since the introduction of deep neural networks (DNNs), many AI-based …

Advances in automated treatment planning

D Nguyen, MH Lin, D Sher, W Lu, X Jia… - Seminars in radiation …, 2022‏ - Elsevier
Treatment planning in radiation therapy has progressed enormously over the past several
decades. Such advancements came in the form of innovative hardware and algorithms …

Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder‐decoder network

G Heilemann, L Zimmermann, R Schotola… - Medical …, 2023‏ - Wiley Online Library
Background Deep learning‐based auto‐planning is an active research field; however, for
some tasks a treatment planning system (TPS) is still required. Purpose To introduce a deep …

A hierarchical deep reinforcement learning framework for intelligent automatic treatment planning of prostate cancer intensity modulated radiation therapy

C Shen, L Chen, X Jia - Physics in Medicine & Biology, 2021‏ - iopscience.iop.org
Purpose. We have previously proposed an intelligent automatic treatment planning (IATP)
framework that builds a virtual treatment planner network (VTPN) to operate a treatment …

Artificial intelligence‐based radiotherapy machine parameter optimization using reinforcement learning

WT Hrinivich, J Lee - Medical physics, 2020‏ - Wiley Online Library
Purpose To develop and evaluate a volumetric modulated arc therapy (VMAT) machine
parameter optimization (MPO) approach based on deep‐Q reinforcement learning (RL) …