Brain Cancer

PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.

OBJECTIVES: This study aimed to develop and validate a clinically motivated artificial intelligence framework for preoperative risk assessment of the second mesiobuccal (MB2) canal in maxillary first molars using panoramic radiographs. MATERIALS AND METHODS: A total of 388 panoramic radiographs were retrospectively collected. Stage 1 used YOLOv5 to localize maxillary first molars and crop tooth-le...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for meningioma.

BACKGROUND: Prescription dose selection for intracranial meningiomas treated with stereotactic radiosurgery remains guided by tumor volume, anatomical constraints, proximity to critical structures, and institutional practice rather than individualized estimates of long-term local progression. We developed THINKERS-Meningioma, a mixture-of-experts artificial intelligence framework for personalized ...

Radiation-induced erectile dysfunction in prostate cancer: A systematic review of pathophysiology, clinical radiobiology, and predictive modeling.

BACKGROUND: Radiation-induced erectile dysfunction (RIED) remains one of the most prevalent and distressing late toxicities among prostate cancer surv...

Therapeutic potential of pachymic acid in glioma: a comprehensive approach combining experimental validation, network pharmacology, and machine learning.

Pachymic acid (PA) is a natural active component of Poria cocos(Schw.)Wol. Although PA exhibits antitumor activity in multiple cancers, its effects an...

Evaluating Risk Factors Associating With Late Recurrence After Low-Dose-Rate Brachytherapy for Prostate Cancer Using Machine Learning.

BACKGROUND: The Phoenix definition is widely used to define biochemical recurrence (BCR) after radiation therapy for prostate cancer; however, a defin...

Retraction: Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Integrated multi-omics and machine learning identify OGDHL as a key regulator of glioblastoma progression and antitumor immunity via glutamine metabolism and histone lactylation.

BACKGROUND: Glioblastoma (GBM) has an extremely poor prognosis, and its malignant progression is closely associated with glutamine metabolic reprogram...

A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.

BACKGROUND: Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...

Explainable hourly global solar radiation forecasting using a CNN-BiLSTM model with temperature scaled softmax attention.

Accurate global solar radiation (GSR) forecasting is vital for smart grids and resilient energy systems. However, the nonlinear and non-stationary nat...

Enhanced MRI brain tumor segmentation with DNet and hybrid dice-weighted cross-entropy loss.

Localization of brain tumors via magnetic resonance imaging (MRI) is critically important for diagnosis, treatment, and surgical operations. This stud...

Integrative single-cell and spatial transcriptomic analysis reveals a lactate-driven crosstalk between NFATc4⁺ tumor cells and SPP1⁺ macrophages in glioblastoma.

BACKGROUND: Glioblastoma (GBM) remains a lethal brain tumor with limited therapeutic options. Metabolic reprogramming, particularly lactate metabolism...

Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.

BACKGROUND: Aortic valve calcium scoring by computed tomography (CT) is an established method for assessing aortic stenosis severity but is limited by...

RadiolGAN: Multicenter Feasibility Study of Synthetic CT From 3D Ultra-Short Echo Time MRI for Enhanced Pulmonary Radiologic Sign Visualization.

BACKGROUND: Chest CT requires breath-holding and ionizing radiation. 3D ultrashort echo time (UTE) MRI allows radiation-free imaging, but the image qu...

Center-specific Federated Learning for Radiation Pneumonitis:A Cross-Center Adaptive Alternating Framework.

BACKGROUND AND PURPOSE: Accurate prediction of symptomatic radiation pneumonitis (RP) is critical for radiotherapy, yet the generalization of deep lea...

Role of MRI-based synthetic CT and low-dose CT for the assessment of structural damage in axial spondyloarthritis - a review.

Structural lesions, including erosions, sclerosis, and pathological new bone formation, are key features of disease progression in axial spondyloarthr...

Dual-view fusion of log-mel spectrogram encodings for dysarthria detection: use in the context of awake brain surgery.

PURPOSE: Intraoperative speech monitoring during awake glioma surgery is critical to detect stimulation-induced motor speech impairments such as dysar...

Therapeutic Hybrid Intelligence with Neural and Knowledge-based Expert Reasoning for SRS (THINKERS): an AI model for GBM.

BACKGROUND: Prescription dose selection for recurrent glioblastoma treated with Gamma Knife radiosurgery remains highly individualized and is largely ...

Time-dependent radiological risk assessment following a severe NPP accident using Monte Carlo simulation, BEIR VII, and interpretable AI.

Nuclear reactor accidents can result in prolonged radiation exposure with complex and uncertain health consequences. Existing studies often focus on d...

Machine-Learning-Based Prediction of Clinical Outcomes in Gliomas Using Glycomic Features.

Background: Gliomas are primary malignant brain tumors. Among them, grade IV astrocytomas represent the most aggressive form with limited treatment op...