Brain Cancer

Radiomics-guided multimodal MRI fusion framework for non-enhanced glioblastoma noninvasive identification.

This study aimed to develop and externally validate a radiomics-based machine learning framework for the noninvasive differentiation of non-enhanced glioblastoma (NE-GBM) from astrocytoma, IDH-mutant, grade 2 (IDHm-A2), thereby addressing the diagnostic limitations of qualitative MRI and the challenge of data scarcity. This diagnostic study used a radiomics-based machine learning framework, with i...

Automated deep learning-based segmentation and volumetric analysis of meningiomas.

INTRODUCTION: Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based model for the automated segmentation of meningiomas and associated peritumoral ed...

Two-stage deep learning framework for magnetic resonance imaging-based detection of temporomandibular joint osteoarthritis.

OBJECTIVES: Cone-beam computed tomography (CBCT) is the reference standard for detecting osseous changes in temporomandibular joint osteoarthritis (TM...

Intelligent Organ Recognition Technology Combined With an Artificial Intelligence Iterative Reconstruction Algorithm in Contrast-enhanced CT of the Chest, Abdomen, and Pelvis.

RATIONALE AND OBJECTIVES: To evaluate the application value of intelligent organ recognition technology combined with the artificial intelligence iter...

[A semi-supervised MRI image segmentation dual-network model for regions with ambiguous boundaries and heterogeneous regions].

OBJECTIVES: To construct a high-value region-guided dual-network semi-supervised segmentation method (HVASS) under extremely low labeling rates for ad...

Radiomics using generative adversarial network enhanced non-contrast computed tomography for gastric cancer diagnosis.

Objective.Non-contrast-enhanced computed tomography (NCCT) images have limited tissue resolution for gastric cancer diagnosis, while contrast-enhanced...

Global trends and emerging frontiers in robot-assisted vascular intervention: a comprehensive bibliometric analysis.

Robot-assisted vascular intervention may reduce occupational radiation exposure, improve procedural stability, and expand access to specialized endova...

Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.

Brain cancer is one of the most challenging malignancies and a major contributor to worldwide morbidity and mortality. Glioblastoma, the most aggressi...

Big data in U.S. neuro-oncology: trends and translational priorities.

PURPOSE: Neuro-oncology generates complex clinical, imaging, and molecular data, yet datasets remain relatively small and fragmented across modalities...

Prediction of Side Effects from Breast Radiation Therapy - Integration of Clinical and Genomic Data.

OBJECTIVES: To compare the ability of different machine learning models to predict the risk of side effects in patients with breast cancer undergoing ...

Integrating Cardiovascular Screening in Lung Cancer Screening.

Cardiovascular risk assessment is a natural extension of lung cancer screening (LCS) because individuals eligible for low-dose computed tomography oft...

Next generation graphene based MXene metamaterial multicylinder resonator solar absorber optimized with machine learning for conceptual solar dryer mining applications.

In the field of industry and mining sectors, energy demand is rapidly increasing, and it is very necessary to develop a durable, scalable, and broadba...

Multiscale Multiparametric MRI Deep Learning for Short-Term Survival Assessment in Glioblastoma.

BACKGROUND: Preoperative identification of short-term survival in glioblastoma may guide management but remains challenging because of clinical and im...

scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.

MOTIVATION: Cross-modal translation enables reconstruction of missing modalities in single-cell multi-omics data, supporting integrative analyses of c...

High precision segmentation of glioma and surroundings - a feasibility study using multiparametric MRI and deep learning.

Glioblastoma (GBM) is an aggressive primary brain cancer in which precise spatial characterisation of tumour sub-compartments and surrounding anatomy ...

From optimization of radiation dose distributions to optimal cancer treatments.

The history of optimization in radiation oncology is closely intertwined with the history of Physics in Medicine & Biology (PMB). Over the past four d...

Construction of a prognostic model using lactylation-related genes for predicting the prognosis of glioma patients.

Gliomas are the most lethal malignant tumors of the central nervous system, and their treatment continues to face serious challenges. Increasing evide...

Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.

BACKGROUND AND OBJECTIVES: Machine learning (ML) and natural language processing (NLP) approaches are increasingly used to support nuanced phenotyping...

Dosimetric parameter-based machine learning to predict radiation pneumonitis following breast radiotherapy.

Objective.Radiation pneumonitis (RP) is an important toxicity following breast radiotherapy. Although modern treatment techniques limit lung exposure,...

Postoperative Nocardia cyriacigeorgica infection after glioblastoma resection: correlating metagenomic next-generation sequencing with conventional microbiology.

Postoperative nocardial infection after cranial surgery is rare and difficult to diagnose because Nocardia spp. grow slowly in conventional culture. M...