Therapeutic Radiology

Optimisation and validation of a co-manipulated robot for brachytherapy procedure.

BACKGROUND: Low-dose rate brachytherapy is the referent treatment for early-stage prostate cancer and consists in manually inserting radioactive seeds within the organ to destroy tumorous cells. This treatment is inaccurate leading to side effects. Researchers developed robots to improve this technique. Despite ameliorating accuracy, they cannot be clinically used because of size and acceptability...

Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer.

BACKGROUND AND PURPOSE: This study aims to investigate how accurate our deep learning (DL) dose prediction models for intensity modulated radiotherapy (IMRT) and pencil beam scanning (PBS) treatments, when chained with normal tissue complication probability (NTCP) models, are at identifying esophageal cancer patients who are at high risk of toxicity and should be switched to proton therapy (PT).

A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.

PURPOSE: Fast and accurate outlining of the organs at risk (OARs) and high-risk clinical tumor volume (HRCTV) is especially important in high-dose-rat...

Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer.

Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brach...

Systematic Clinical Evaluation of a Deep Learning Method for Medical Image Segmentation: Radiosurgery Application.

We systematically evaluate a Deep Learning model in a 3D medical image segmentation task. With our model, we address the flaws of manual segmentation:...

Treatment plan prediction for lung IMRT using deep learning based fluence map generation.

PURPOSE: Recently, it has been shown that automated treatment planning can be executed by direct fluence prediction from patient anatomy using convolu...

Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy.

PURPOSE: To automate the segmentation of treatment applicators on computed tomography (CT) images for high-dose-rate (HDR) brachytherapy prostate pati...

The challenges facing deep learning-based catheter localization for ultrasound guided high-dose-rate prostate brachytherapy.

BACKGROUND: Automated catheter localization for ultrasound guided high-dose-rate prostate brachytherapy faces challenges relating to imaging noise and...

Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of mach...

A feasibility study for in vivo treatment verification of IMRT using Monte Carlo dose calculation and deep learning-based modelling of EPID detector response.

BACKGROUND: This paper describes the development of a predicted electronic portal imaging device (EPID) transmission image (TI) using Monte Carlo (MC)...

Automatic segmentation of magnetic resonance images for high-dose-rate cervical cancer brachytherapy using deep learning.

PURPOSE: Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target...

Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor.

Using deep learning models to analyze patients with intracranial tumors, to study the image segmentation and standard results by clinical depiction co...

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation.

Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise t...

Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

Stereotactic radiosurgery planning for cerebral arteriovenous malformations (AVM) is complicated by the variability in appearance of an AVM nidus acro...

Deep-learning-assisted algorithm for catheter reconstruction during MR-only gynecological interstitial brachytherapy.

Magnetic resonance imaging (MRI) offers excellent soft-tissue contrast enabling the contouring of targets and organs at risk during gynecological inte...

The Long-Term Effect of Intensity Modulated Radiation Therapy for Prostate Cancer on Testosterone Levels.

PURPOSE: Concern about a long-term effect of the delivery of intensity modulated radiation therapy (IMRT) for prostate cancer on serum testosterone le...

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm.

Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist p...

Development of a tracking error prediction system for the CyberKnife Synchrony Respiratory Tracking System with use of support vector regression.

PURPOSE: The accuracy of the CyberKnife Synchrony Respiratory Tracking System is dependent on the breathing pattern of a patient. Therefore, the track...

A new prognostic score for predicting survival in patients treated with robotic stereotactic radiotherapy for brain metastases.

The study aimed to analyze potential prognostic factors in patients treated with robotic radiosurgery for brain metastases irrespective of primary tum...

Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy.

PURPOSE: High-dose-rate (HDR) prostate brachytherapy involves treatment catheter placement, which is currently empirical and physician dependent. The ...