Therapeutic Radiology

Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.

BACKGROUND: Segmentation of the Gross Tumor Volume (GTV) is a crucial step in the brachytherapy (BT) treatment planning workflow. Currently, radiation oncologists segment the GTV manually, which is time-consuming. The time pressure is particularly critical for BT because during the segmentation process the patient waits immobilized in bed with the applicator in place. Automatic segmentation algori...

Artificial intelligence applications in brachytherapy: A literature review.

PURPOSE: Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field.

Fastcalculation in LDR brachytherapy using deep learning methods.

The Monte Carlo (MC) method provides a complete solution to the tissue heterogeneity effects in low-energy low-dose rate (LDR) brachytherapy. However,...

Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.

PURPOSE: Artificial intelligence-based tools can be leveraged to improve detection and segmentation of brain metastases for stereotactic radiosurgery ...

Quality of life in patients who underwent robot-assisted radical prostatectomy compared with those who underwent low-dose-rate brachytherapy.

BACKGROUND: To compare the quality of life (QOL) in patients who underwent robot-assisted radical prostatectomy (RARP) or low-dose-rate brachytherapy ...

TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.

BACKGROUND: Intensity-Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT ...

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.

The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate...

Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow.

. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmenta...

A preliminary study on rectal dose reduction associated with hyaluronic acid implantation in brachytherapy for prostate cancer.

OBJECTIVES: Hydrogel spacer (HS) was developed to reduce rectal toxicities caused by radiotherapy, but has been reported to cause major adverse events...

A novel mathematical model to generate semi-automated optimal IMRT treatment plan based on predicted 3D dose distribution and prescribed dose.

BACKGROUND: In recent years, with the development of artificial intelligence and deep learning techniques, it has become possible to predict the three...

3D dose prediction for Gamma Knife radiosurgery using deep learning and data modification.

PURPOSE: To develop a machine learning-based, 3D dose prediction methodology for Gamma Knife (GK) radiosurgery. The methodology accounts for cases inv...

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

BACKGROUND: Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a...

A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention.

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. It...

Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.

PURPOSE: The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (I...

Respiratory motion prediction based on deep artificial neural networks in CyberKnife system: A comparative study.

BACKGROUND: In external beam radiotherapy, a prediction model is required to compensate for the temporal system latency that affects the accuracy of r...

Deep Learning-based Non-rigid Image Registration for High-dose Rate Brachytherapy in Inter-fraction Cervical Cancer.

In this study, an inter-fraction organ deformation simulation framework for the locally advanced cervical cancer (LACC), which considers the anatomica...

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.

In this work we present a framework for robust deep learning-based VMAT forward dose calculations for the 1.5T MR-linac. A convolutional neural networ...

The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data.

Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it play...

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy.

. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation t...

Gradual Self-Training via Confidence and Volume Based Domain Adaptation for Multi Dataset Deep Learning-Based Brain Metastases Detection Using Nonlocal Networks on MRI Images.

BACKGROUND: Research suggests that treatment of multiple brain metastases (BMs) with stereotactic radiosurgery shows improvement when metastases are d...