Brain Cancer

Pembrolizumab alters the tumor immune landscape in a patient with dMMR glioblastoma.

Congenital DNA mismatch repair defects (dMMR), such as Lynch Syndrome, predispose patients to a vari...

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

This review article highlights the potential of integrating photon-counting computed tomography (CT)...

Increased Peripheral Blood DNA Damage and Elevated Serum Levels of Melanoma Inhibitory Activity Protein: Clues to Excess Skin Cancer Risk in Airline Pilots?

Background and objective The risk of malignant melanoma (MM) and other forms of skin cancer appears ...

Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.

BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditi...

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model.

PURPOSE: Computed tomography (CT) scans are a significant source of medically induced radiation expo...

Intraoperative molecular diagnosis of glioma through combination of radiofrequency signals from ultrasound and deep learning.

A Short Review on the Impact of Artificial Intelligence in Diagnosis Diseases: Role of Radiomics In Neuro-Oncology.

Artificial Intelligence (AI) is rapidly transforming various aspects of healthcare, including the fi...

StemnesScoRe: an R package to estimate the stemness of glioma cancer cells at single-cell resolution.

BACKGROUND/AIM: Glioblastoma is the most heterogeneous and the most difficult-to-treat type of brain...

Artificial intelligence in neuro-oncology.

Artificial intelligence (AI) describes the application of computer algorithms to the solution of pro...

Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.

PURPOSE: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) m...

An End-to-end and Drug Repurposing Pipeline for Glioblastoma.

Our study aims to address the challenges in drug development for glioblastoma, a highly aggressive b...

Rapid visualization of PD-L1 expression level in glioblastoma immune microenvironment via machine learning cascade-based Raman histopathology.

INTRODUCTION: Combination immunotherapy holds promise for improving survival in responsive glioblast...

Single-Cell Radiation Response Scoring with the Deep Learning Algorithm CeCILE 2.0.

External stressors, such as ionizing radiation, have massive effects on life, survival, and the abil...

Deep Learning-Guided Dosimetry for Mitigating Local Failure of Patients With Non-Small Cell Lung Cancer Receiving Stereotactic Body Radiation Therapy.

PURPOSE: Non-small cell lung cancer (NSCLC) stereotactic body radiation therapy with 50 Gy/5 fractio...

Meningioma consistency assessment based on the fusion of deep learning features and radiomics features.

PURPOSE: This study aims to combine deep learning features with radiomics features for the computer-...

Rapid intraoperative multi-molecular diagnosis of glioma with ultrasound radio frequency signals and deep learning.

BACKGROUND: Molecular diagnosis is crucial for biomarker-assisted glioma resection and management. H...

Energy controls wave propagation in a neural network with spatial stimuli.

Nervous system has distinct anisotropy and some intrinsic biophysical properties enable neurons pres...

Glioma Tumor Grading Using Radiomics on Conventional MRI: A Comparative Study of WHO 2021 and WHO 2016 Classification of Central Nervous Tumors.

BACKGROUND: Glioma grading transformed in World Health Organization (WHO) 2021 CNS tumor classificat...

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radia...

Multicenter Study of the Utility of Convolutional Neural Network and Transformer Models for the Detection and Segmentation of Meningiomas.

PURPOSE: This study aimed to investigate the effectiveness and practicality of using models like con...

Clinical and Dosimetric Impact of 2D kV Motion Monitoring and Intervention in Liver Stereotactic Body Radiation Therapy.

PURPOSE: Positional errors resulting from motion are a principal challenge across all disease sites ...