Radiology

Therapeutic Radiology

Latest AI and machine learning research in therapeutic radiology for healthcare professionals.

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Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.

Predicting the probability of having the plan approved by the physician is important for automatic t...

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...

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 volu...

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...

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...

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 patien...

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 fo...

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 mi...

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 prosta...

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 (H...

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) ...

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 pred...

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 need...

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 ...

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 ...

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 lun...

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 ch...

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 acce...

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