AIMC Topic: Radiotherapy Dosage

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Integration of the M6 Cyberknife in the Moderato Monte Carlo platform and prediction of beam parameters using machine learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This work describes the integration of the M6 Cyberknife in the Moderato Monte Carlo platform, and introduces a machine learning method to accelerate the modelling of a linac.

Knowledge-Based Tradeoff Hyperplanes for Head and Neck Treatment Planning.

International journal of radiation oncology, biology, physics
PURPOSE: To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning.

Incorporating human and learned domain knowledge into training deep neural networks: A differentiable dose-volume histogram and adversarial inspired framework for generating Pareto optimal dose distributions in radiation therapy.

Medical physics
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 training of deep neural networks. In this study, we trained a neural n...

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation.

Medical physics
PURPOSE: Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the ac...

A hybrid automated treatment planning solution for esophageal cancer.

Radiation oncology (London, England)
OBJECTIVE: This study aims to investigate a hybrid automated treatment planning (HAP) solution that combines knowledge-based planning (KBP) and script-based planning for esophageal cancer.

Evaluation of complexity and deliverability of prostate cancer treatment plans designed with a knowledge-based VMAT planning technique.

Journal of applied clinical medical physics
PURPOSE: Knowledge-based planning (KBP) techniques have been reported to improve plan quality, efficiency, and consistency in radiation therapy. However, plan complexity and deliverability have not been addressed previously for treatment plans guided...

Liver tumor segmentation based on 3D convolutional neural network with dual scale.

Journal of applied clinical medical physics
PURPOSE: Liver is one of the organs with a high incidence of tumors in the human body. Malignant liver tumors seriously threaten human life and health. The difficulties of liver tumor segmentation from computed tomography (CT) image are: (a) The cont...

Knowledge-based automated planning with three-dimensional generative adversarial networks.

Medical physics
PURPOSE: To develop a knowledge-based automated planning pipeline that generates treatment plans without feature engineering, using deep neural network architectures for predicting three-dimensional (3D) dose.

Winter is over: The use of Artificial Intelligence to individualise radiation therapy for breast cancer.

Breast (Edinburgh, Scotland)
Artificial intelligence demonstrated its value for automated contouring of organs at risk and target volumes as well as for auto-planning of radiation dose distributions in terms of saving time, increasing consistency, and improving dose-volumes para...

A simple knowledge-based tool for stereotactic radiosurgery pre-planning.

Journal of applied clinical medical physics
We studied the dosimetry of single-isocenter treatment plans generated to treat a solitary intracranial lesion using linac-based stereotactic radiosurgery (SRS). A common metric for evaluating SRS plan quality is the volume of normal brain tissue irr...