Therapeutic Radiology

Towards robust deep learning-based autosegmentation in MRI-planned gynecological brachytherapy: Importance of scalable development and comprehensive evaluation.

PURPOSE: To present comprehensive development and evaluation methodologies for a generalizable deep learning (DL)-driven autocontouring model of standard pelvic organs-at-risk (OARs) in MRI-planned cervical brachytherapy. MATERIALS AND METHODS: A curated dataset of 200 3D-MRIs (85% training/validation, 15% testing) including multiple applicator types, varying treated anatomies, and manual contours...

3D transformer-based dose prediction in HDR brachytherapy for cervical cancer.

AI-based assessment of brain volume decrease after treatment with stereotactic radiosurgery versus whole brain radiotherapy.

BACKGROUND: Radiotherapy is a cornerstone in the treatment of brain metastases, but its mid- and long-term impact on brain parenchyma remains poorly u...

Building a national repository of dural-based lesions: clinical, pathological, and demographic insights from the Indian population.

BACKGROUND: Meningiomas are the most common dural-based intracranial tumors, yet Indian literature is predominantly composed of limited single-center ...

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): outcome prediction with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults and present ongoing challenges in clinical management, particularl...

Implementation of deep learning with convolutional block attention module for detecting collimator rotation errors in stereotactic radiosurgery quality assurance.

Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

PURPOSE: To develop and validate a multimodal ensemble machine learning model integrating multi-sequence magnetic resonance imaging (MRI) radiomics, c...

Development and external validation of an MRI radiomics-based machine learning model to predict tumour recurrence in brain metastases treated with Gamma Knife radiosurgery.

BACKGROUND: Predicting recurrence after gamma knife radiosurgery (GKRS) is clinically important, as it informs salvage treatment and patient managemen...

Evaluation of AI chatbot responses to brachytherapy frequently asked questions.

PURPOSE: Patients are increasingly using artificial intelligence (AI) chatbots for health information. Evaluating their reliability for specialized to...

Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group.

BACKGROUND: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for several primary and metastatic malignancies; however, c...

Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis.

PURPOSE: Stereotactic radiosurgery (SRS) is a standard treatment for brain metastases; however, it may lead to radiation necrosis (RN). RN can be virt...

Applicator reconstruction in cervical cancer brachytherapy: A systematic review of current methods, challenges, and AI-driven future directions.

PURPOSE: Accurate applicator reconstruction is a critical step in 3D image-guided brachytherapy (3D-IGBT) for cervical cancer, directly influencing tu...

Automated quality assurance of imaging dose and protocol adherence in computed tomography radiotherapy planning using TotalSegmentator-based segmentation.

PURPOSE: Computed tomography (CT) scans are vital for radiotherapy planning, providing essential data for dose calculations. This study retrospectivel...

An Explainable Deep Model for Risk Scoring and Accurate Radionecrosis Identification Following Brain Metastasis Stereotactic Radiosurgery.

PURPOSE: As survival improves for patients with brain metastases (BM), distinguishing local recurrence (LR) from radionecrosis (RN) is a growing neuro...

A hybrid decision support system using rule-based and AI methods: the OnCATs knowledge-based framework.

BACKGROUND AND SIGNIFICANCE: Clinical decision support systems (CDSS) can improve evidence-based oncology care, but many rely on opaque AI models that...

Performance of a Novel Frameless and Maskless Robotic Head Motion Compensation System for Stereotactic Radiosurgery in a Realistic Clinical Environment with Healthy Volunteers.

PURPOSE: Stereotactic radiosurgery (SRS) is a nonsurgical method for treating brain abnormalities and small tumors. Traditional high-accuracy SRS requ...

Non-invasive prediction of the secondary enucleation risk in uveal melanoma based on pretreatment CT and MRI prior to stereotactic radiotherapy.

PURPOSE: The aim of this study was to develop a radiomic model to non-invasively predict the risk of secondary enucleation (SE) in patients with uveal...

Beam orientation optimization in IMRT using sparse mixed integer programming and non-convex fluence map optimization.

Beam orientation optimization (BOO) in intensity-modulated radiation therapy (IMRT) is a complex, non-convex problem traditionally addressed with heur...

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 lea...

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...