AIMC Topic: Radiotherapy, Intensity-Modulated
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Robotic MLC-based plans: A study of plan complexity.
Medical physics
Jan 21, 2021
PURPOSE: The utility of complexity metrics has been assessed for IMRT and VMAT treatment plans, but this analysis has never been performed for CyberKnife (CK) plans. The purpose of this study is to perform a complexity analysis of CK MLC plans, adapt...
DVH Prediction for VMAT in NPC with GRU-RNN: An Improved Method by Considering Biological Effects.
BioMed research international
Jan 19, 2021
PURPOSE: A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information g...
Data-driven dose calculation algorithm based on deep U-Net.
Physics in medicine and biology
Dec 22, 2020
Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation algorithms commonly used in current clinical practice have to compromise between calculation accuracy a...
Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Nov 29, 2020
OBJECTIVE: Dose prediction using deep learning networks prior to radiotherapy might lead tomore efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess thei...
Deep learning-based inverse mapping for fluence map prediction.
Physics in medicine and biology
Nov 27, 2020
We developed a fluence map prediction method that directly generates fluence maps for a given desired dose distribution without optimization for volumetric modulated arc therapy (VMAT) planning. The prediction consists of two steps. First, projection...
Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance.
International journal of radiation oncology, biology, physics
Nov 13, 2020
PURPOSE: Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona vir...
Artificial intelligence-based radiotherapy machine parameter optimization using reinforcement learning.
Medical physics
Nov 5, 2020
PURPOSE: To develop and evaluate a volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) approach based on deep-Q reinforcement learning (RL) capable of finding an optimal machine control policy using previous prostate cancer p...
Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers.
JAMA network open
Nov 2, 2020
IMPORTANCE: Personalized radiotherapy planning depends on high-quality delineation of target tumors and surrounding organs at risk (OARs). This process puts additional time burdens on oncologists and introduces variability among both experts and inst...
Using deep learning to model the biological dose prediction on bulky lung cancer patients of partial stereotactic ablation radiotherapy.
Medical physics
Oct 26, 2020
PURPOSE: To develop a biological dose prediction model considering tissue bio-reactions in addition to patient anatomy for achieving a more comprehensive evaluation of tumor control and promoting the automatic planning of bulky lung cancer.
Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 22, 2020
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 to adapt the model, via transfer learning with minimal input data, t...
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