Therapeutic Radiology

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS).

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy.

In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Data from 200 pros...

Phantom study of a fully automatic radioactive seed placement robot for the treatment of skull base tumours.

BACKGROUND: Interstitial brachytherapy is a form of intensive local irradiation that facilitates the effective protection of surrounding structures an...

Explainable artificial intelligence analysis of brachytherapy boost receipt in cervical cancer during the COVID-19 era.

PURPOSE: Brachytherapy is a critical component of the standard-of-care curative radiotherapy regimen for women with locally advanced cervical cancer (...

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

PURPOSE: To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brac...

High-dose-rate Brachytherapy Monotherapy in Patients With Localised Prostate Cancer: Dose Modelling and Optimisation Using Computer Algorithms.

AIMS: Interstitial high-dose-rate brachytherapy (HDR-BT) is an effective therapy modality for patients with localized prostate carcinoma. The objectiv...

Two is better than one: longitudinal detection and volumetric evaluation of brain metastases after Stereotactic Radiosurgery with a deep learning pipeline.

PURPOSE: Close MRI surveillance of patients with brain metastases following Stereotactic Radiosurgery (SRS) treatment is essential for assessing treat...

Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

. Small-field dosimetry is an ongoing challenge in radiotherapy quality assurance (QA) especially for radiosurgery systems such as CyberKnife. The obj...

Hydrogel spacer injection to the meso-sigmoid to protect the sigmoid colon in cervical cancer brachytherapy: A technical report.

PURPOSE: The use of a hydrogel spacer inserted into recto-vaginal fossa is a valuable strategy to mitigate radiation exposure to the rectum during rad...

Toward a deep learning-based magnetic resonance imaging only workflow for postimplant dosimetry in I-125 seed brachytherapy for prostate cancer.

BACKGROUND AND PURPOSE: The current standard imaging-technique for creating postplans in seed prostate brachytherapy is computed tomography (CT), that...

Estimating blurless and noise-free Ir-192 source images from gamma camera images for high-dose-rate brachytherapy using a deep-learning approach.

. Precise monitoring of the position and dwell time of iridium-192 (Ir-192) during high-dose-rate (HDR) brachytherapy is crucial to avoid serious dama...

Biochemical outcome after curative treatment for localized prostate cancer with external beam radiotherapy: a cross-sectional study.

Although many patients who receive definitive radiotherapy (RT) for localised prostate cancer (CaP) experience long-term disease-free survival and bet...

Predictive modeling of dose-volume parameters of carcinoma tongue cases using machine learning models.

The aim of this study is to create a single institution-based machine learning model for a dose prediction generation tool for post-operative carcinom...

Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy.

BACKGROUND: Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. Thi...

Deep learning-based ultrasound auto-segmentation of the prostate with brachytherapy implanted needles.

BACKGROUND: Accurate segmentation of the clinical target volume (CTV) corresponding to the prostate with or without proximal seminal vesicles is requi...

Novel Solution for Using Neural Networks for Kidney Boundary Extraction in 2D Ultrasound Data.

: Kidney ultrasound (US) imaging is a significant imaging modality for evaluating kidney health and is essential for diagnosis, treatment, surgical in...

Predicting successful clinical candidates for fiducial-free lung tumor tracking with a deep learning binary classification model.

OBJECTIVES: The CyberKnife system is a robotic radiosurgery platform that allows the delivery of lung SBRT treatments using fiducial-free soft-tissue ...

Deep learning-based dose map prediction for high-dose-rate brachytherapy.

. Creating a clinically acceptable plan in the time-sensitive clinic workflow of brachytherapy is challenging. Deep learning-based dose prediction tec...

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of ...

Performance assessment of variant UNet-based deep-learning dose engines for MR-Linac-based prostate IMRT plans.

. UNet-based deep-learning (DL) architectures are promising dose engines for traditional linear accelerator (Linac) models. Current UNet-based engines...