Brain Cancer

Real-time intraoperative confocal laser endomicroscopy with on-site neuropathologist consultation for glioma margin assessment: illustrative case.

BACKGROUND: Achieving maximal safe resection in glioma surgery requires accurate real-time margin assessment, yet existing technologies have limitations. Fluorescence-guided surgery shows limitations at infiltrative margins, while frozen section analysis requires 20-45 minutes per specimen. Confocal laser endomicroscopy (CLE) is an emerging in vivo imaging technology that enables real-time cellula...

Mask - Region-based Convolutional Neural Networks (R-CNN) with Radiomics Integration and Gray Level Co-occurrence Matrix (GLCM) for brain tumor detection and segmentation.

Early diagnosis of brain tumors is important for successful treatment and better patient consequences in industrial information systems. This research employs Mask Region-based Convolutional Neural Networks (R-CNN), radiomics integration, and the Gray Level Co-occurrence Matrix (GLCM) to progress brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The Mask R-CNN i...

Transformer-based architectures in MRI brain tumor segmentation: A review.

Transformers have been actively utilized in the deep learning field recently. Vision Transformer (ViT), as one of its important applications in the co...

Flow rate determination in a two-phase system using radioactive particle tracking and deep learning.

In the oil industry, accurate flow rate determination and control in pipelines are critical for ensuring operational efficiency. However, most convent...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and enhancing patients' quality of life. As the third m...

Neoadjuvant Radiation is Causally Linked to Increased Operative Time and Perioperative Blood Transfusion in Pancreatic Ductal Adenocarcinoma.

INTRODUCTION: Blood transfusion in patients undergoing surgical resection for pancreatic ductal adenocarcinoma (PDAC) is associated with worse outcome...

Hybrid deep neural network with PCA based features optimization for enhancing brain tumor classification.

Brain tumors have been an important medical concern. This is primarily due to their growth patterns have been hard to predict and their medical needs ...

Prediction of Symptomatic Radiation Pneumonitis in Lung Cancer Patients: A Radiomics and Dosiomics Machine Learning Approach Using the Prospective Multicenter RTOG 0617 and REQUITE Trials.

PURPOSE: Radiation-induced pneumonitis (RP) is a side effect after thoracic radiation therapy (RT). The ability to predict RP would facilitate treatme...

Integrative multi-platform gene expression and machine learning analysis for glioblastoma biomarker discovery with experimental validation.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive form of brain tumor with poor prognosis. This study aimed to identify genes critical to glioma d...

Use of a Novel Intraoperative Neurocognitive Monitoring Task During Awake Craniotomy for Tumor Resection in the Language-Dominant Parietal Lobe.

BACKGROUND AND OBJECTIVES: The identification of eloquent structures in the parietal lobe during awake craniotomy remains challenging, particularly fo...

Contemporary Management of Radiation Necrosis: Insights and Avenues.

Radiation necrosis (RN) remains a challenging complication of upfront radiation therapy for both brain metastases and primary tumors. Despite developm...

Artificial Intelligence for radiation dose reduction in computed tomography: a narrative synthesis of clinical evidence from 2020 to 2025.

Computed tomography (CT) is essential to modern clinical practice but contributes substantially to population radiation exposure, particularly in onco...

Addressing the balance between fairness and performance in glioma grade prediction using bias mitigation techniques.

Single-cell systems network and machine learning dissection of ferroptosis in recurrent glioblastoma highlights astrocytic ZFP36 as a therapeutic hub for network medicine repurposing.

BACKGROUND: Recurrent glioblastoma multiforme (rGBM) arises after conventional treatment strategies for primary GBM (pGBM) fail, leading to a more agg...

CanSig Benchmarks Methods for Reproducible Cancer Cell State Discovery from Single-Cell Transcriptomic Data.

UNLABELLED: Single-cell RNA sequencing facilitates the discovery of gene expression signatures that define cell states across patients, which could be...

Emerging Roles of Multiparametric Biodosimetry and Artificial Intelligence in Advancing Radiation Dose Assessment for Triage.

Biodosimetry plays a crucial role in radiation emergency preparedness and response by enabling efficient allocation of medical resources through prior...

A Flexible Hybrid Quantum-classical Training Framework of Organ-at-Risk and Tumor Segmentation Models for Radiation Therapy Planning.

Deep learning-based Organ-at-Risk (OAR) and tumor segmentation is vital for radiation therapy planning but often suffers from over-parameterization, r...

Brain tumor classification using optimized ResNet50 with dynamic precision optimization for enhanced speed and diagnostic accuracy.

The detection and classification of brain tumors remain a major challenge in the medical field due to their morphological complexity. In this study, b...

Multimodal hybrid mamba classification model for tumor pathological grade prediction using magnetic resonance images.

Malignant tumors present a significant global health challenge, and accurate pathological grading is essential for personalized treatment. Traditional...

Interpretable prognostic modeling of glioblastoma using cross-cohort transcriptome integration and machine learning approaches.

Glioblastoma (GBM) is an aggressive brain tumor with highly variable patient outcomes due to pronounced molecular heterogeneity. Prognosis remains dis...