Brain Cancer

Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition.

Glioblastoma (GBM) presents significant therapeutic challenges due to its invasive nature and resist...

Trading off Iodine and Radiation Dose in Coronary Computed Tomography.

Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player i...

Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: a multicenter study.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study ai...

Preoperative Differentiation of Spinal Schwannoma and Meningioma Using Machine Learning-Based Models: A Systematic Review and Meta-Analysis.

BACKGROUND: Regarding the differences in surgical approaches for spinal schwannomas and meningiomas,...

Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.

OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic ...

Non-orthogonal kV imaging guided patient position verification in non-coplanar radiation therapy with dataset-free implicit neural representation.

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiatio...

Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development.

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-ind...

Correction to: Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT‑methylation status, age and sex.

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 m...

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction.

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces r...

MRI-derived deep learning models for predicting 1p/19q codeletion status in glioma patients: a systematic review and meta-analysis of diagnostic test accuracy studies.

PURPOSE: We conducted a systematic review and meta-analysis to evaluate the performance of magnetic ...

Mitochondrial dysfunction and cell death induced by Toona sinensis leaf extracts through MEK/ERK signaling in glioblastoma cells.

Toona sinensis, a kind of phytochemicals in traditional Chinese medicine widely used in South-East A...

An integrated machine learning-based prognostic model in head and neck cancer using the systemic inflammatory response index and correlations with patient reported financial toxicity.

OBJECTIVE: To investigate the prognostic utility of systemic inflammatory response index (SIRI) as a...

Semisupervised adaptive learning models for IDH1 mutation status prediction.

The mutation status of isocitrate dehydrogenase1 (IDH1) in glioma is critical information for the di...

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some stud...

Detecting the left atrial appendage in CT localizers using deep learning.

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example,...

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

This project aimed to develop and evaluate an automated, AI-based, volumetric brain tumor MRI respon...

Reirradiation for recurrent glioblastoma: the significance of the residual tumor volume.

PURPOSE: Recurrent glioblastoma has a poor prognosis, and its optimal management remains unclear. Re...

Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radia...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural card...

A machine learning tool for prediction of vertebral compression fracture following stereotactic body radiation therapy for spinal metastases.

BACKGROUND AND PURPOSE: The most common adverse event following spine stereotactic body radiotherapy...