Therapeutic Radiology

Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery.

BACKGROUND: Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lead to radiation-induced changes (RICs) in the surrounding brain tissue. Volumetric assessment of RICs is crucial for therapy planning and monitoring. RICs that appear as hyper-dense areas in magnetic resonance T2-weighted (T2w) images are clearly iden...

The impact of intensity discretisation and filtration on the performance of the radiomic and machine learning models in brain metastasis patients treated with gamma knife radiosurgery.

INTRODUCTION: Image preprocessing is crucial for optimizing radiomics feature extraction, however, inconsistencies in the implementation process and a lack of universally accepted methods lead to diverse approaches. This study evaluates the impact of radiomics and machine learning (ML) performance in brain metastasis.

Novel Application of Connectomics to the Surgical Management of Pediatric Arteriovenous Malformations.

Introduction The emergence of connectomics in neurosurgery has allowed for construction of detailed maps of white matter connections, incorporating bo...

Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of...

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learn...

Classifying Stereotactic Radiosurgery Patients by Primary Diagnosis Using Natural Language Processing of Clinical Notes.

PURPOSE: Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health ...

Effects of concurrent HER2-directed therapy on development of cerebral radionecrosis after stereotactic radiotherapy: a systematic review.

PURPOSE: With increasing use of human epithelial growth factor receptor two (HER2)-targeted therapies, outcomes for numerous breast cancer patients ha...

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachyther...

Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.

BACKGROUND: Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions be...

Incorporating organ deformation in biological modeling and patient outcome study for permanent prostate brachytherapy.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the ma...

A novel network architecture for post-applicator placement CT auto-contouring in cervical cancer HDR brachytherapy.

BACKGROUND: High-dose-rate brachytherapy (HDR-BT) is an integral part of treatment for locally advanced cervical cancer, requiring accurate segmentati...

Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.

OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data,...

Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.

PURPOSE: Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brac...

A machine learning toolkit assisted approach for IMRT fluence map optimization: feasibility and advantages.

. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-qual...

Deep learning for automated segmentation of brain edema in meningioma after radiosurgery.

BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding br...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (...

Rapid dose prediction for lung CyberKnife radiotherapy plans utilizing a deep learning approach by incorporating dosimetric features delivered by noncoplanar beams.

. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and...

Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.

BACKGROUND: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notabl...

Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.

PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remain...

Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.

BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists t...