Latest AI and machine learning research in therapeutic radiology for healthcare professionals.
Introduction The emergence of connectomics in neurosurgery has allowed for construction of detailed maps of white matter connections, incorporating both structural and functional connectivity patterns. The advantage of mapping cerebral vascular lesions to guide surgical approach shows great potential. We aim to identify the clinical utility of connectomics for the surgical treatment of pediatric a...
BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of NPC patients after intensity-modulated radiation therapy (IMRT) has not been fully studied, and there remains a high risk of recurrence. This study aims to construct a reliable model for predicting post-treatment recurrence by integrating high-accur...
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learn...
PURPOSE: Accurate identification of the primary tumor diagnosis of patients who have undergone stereotactic radiosurgery (SRS) from electronic health ...
PURPOSE: With increasing use of human epithelial growth factor receptor two (HER2)-targeted therapies, outcomes for numerous breast cancer patients ha...
High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachyther...
BACKGROUND: Achieving highly efficient treatment planning in intensity-modulated radiotherapy (IMRT) is challenging due to the complex interactions be...
Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the ma...
BACKGROUND: High-dose-rate brachytherapy (HDR-BT) is an integral part of treatment for locally advanced cervical cancer, requiring accurate segmentati...
OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data,...
PURPOSE: Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brac...
. Traditional machine learning (ML) and deep learning (DL) applications in treatment planning rely on complex model architectures and large, high-qual...
BACKGROUND: Although gamma Knife radiosurgery (GKRS) is commonly used to treat benign brain tumors, such as meningioma, irradiating the surrounding br...
BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (...
. The dose distribution of lung cancer patients treated with the CyberKnife (CK) system is influenced by various factors, including tumor location and...
BACKGROUND: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notabl...
PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remain...
BACKGROUND AND PURPOSE: Accurate segmentation of brain metastases on Magnetic Resonance Imaging (MRI) is tedious and time-consuming for radiologists t...
This study developed and evaluated an automatic segmentation model based on the Mamba framework (AM-UNet) for rapid and precise delineation of high-ri...
Automatic clinical tumor volume (CTV) delineation is pivotal to improving outcomes for interstitial brachytherapy cervical cancer. However, the promin...