PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans.
BACKGROUND: Radiation treatment is considered an effective and the most common treatment option for prostate cancer. The treatment planning process requires accurate and precise segmentation of the prostate and organs at risk (OARs), which is laborio...
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 18, 2022
BACKGROUND AND PURPOSE: To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a ...
PURPOSE: Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target and organs-at-risk (OARs) delineation. Accurate and fast segmentation of MR images are very importa...
PURPOSE: To propose a clinically feasible automatic planning solution for external beam intensity-modulated radiotherapy, including dose prediction via a deep learning and voxel-based optimization strategy.
Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (CBCT) volumes as input is propose...
PURPOSE: To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well-establi...
BACKGROUND: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the pre...
PURPOSE: Typically, the current dose prediction models are limited to small amounts of data and require retraining for a specific site, often leading to suboptimal performance. We propose a site-agnostic, three-dimensional dose distribution predictio...
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