Brain Cancer

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 heal...

Improved virtual non-contrast images in liver dual-energy CT with deep learning algorithm for image reconstruction.

BACKGROUND: We aimed to explore the use of deep learning image reconstruction (DLIR) to improve virtual non-contrast (VNC) images from enhanced dual-energy computed tomography (DECT). MATERIALS AND METHODS: A total of 91 patients undergoing treatment for liver lesions from September to December 2024 were analyzed. All patients underwent true non-contrast (TNC) and dual-phase enhanced DECT with med...

Parametric delineation of the clinical target volume for glioma: Model-based approach that tailors to physician contouring practices.

BACKGROUND AND PURPOSE: Delineating the clinical target volume (CTV) is an essential part of a radiation oncologist's practice. However, this process ...

Extracellular vesicles redefine glioma detection.

Robinson et al. challenge the reliance on neurosurgery for glioma diagnosis by integrating multi-omic profiling of plasma extracellular vesicles with ...

Integrating ex vivo platforms with AI to guide glioblastoma treatment.

PURPOSE: Ex vivo platforms can rapidly and cost-effectively screen patient-derived tumor cells or tissue. Artificial intelligence (AI) algorithms can ...

Identifying optimal dose head CT acquisition techniques through cluster analysis from 904,209 scans.

OBJECTIVES: To identify clinically established and frequently utilized routine head computed tomography (CT) acquisition techniques in children and ad...

Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): a mixture-of-experts framework for gastrointestinal brain metastases.

BACKGROUND: Brain metastases from gastrointestinal (GI) malignancies are heterogeneous, while stereotactic radiosurgery dose selection remains guided ...

Surface Topography and Machine Learning: Strides Towards Radiation-Free Scoliosis Assessment: A Systematic Review.

BACKGROUND: Assessment of idiopathic scoliosis (IS) relies on radiographs and measurement of coronal curve magnitude, but repeated radiation exposure ...

Multicenter machine learning model using clinical and radiomic features for prediction of postoperative residual status in meningioma.

OBJECTIVE: Gross-total resection is the primary surgical objective in meningioma management; however, predicting resectability preoperatively remains ...

The role and mechanism of diosgenin glucoside in modulating megakaryopoiesis through promotion of autophagy via the AMPK/mTOR/ULK1 signaling pathway.

BACKGROUND: Radiation-induced thrombocytopenia (RIT) is a severe, dose-limiting complication of cancer radiotherapy with limited clinical management o...

Deep learning predicts overall survival in high-grade meningioma using MRI and clinical data.

BACKGROUND: High-grademeningiomas (WHO II-III) recurfrequentlyand show wide variation in patient survival,yet clinicians still lack practical ways to ...

Cone Beam Computed Tomography-based Lung Function Imaging using Deep Learning for Functional Image-Guided Adaptative Radiation Therapy.

PURPOSE: Recent studies indicate that lung function can change significantly during radiation therapy (RT) course. However, additional function imagin...

Dual-view scout scans with deep learning for ultra-low dose attenuation correction in PET.

BACKGROUND: Accurate attenuation correction (AC) is essential for quantitative positron emission tomography (PET). Conventional CT-based AC provides r...

A coarse-to-fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning.

BACKGROUND: Boron Neutron Capture Therapy (BNCT) utilizes high linear energy transfer (LET) charged particles from the 10B(n,α)7Li reaction to selecti...

Multi-fidelity Monte Carlo fusion for BNCT dose calculation using a dual-branch network.

BACKGROUND: Boron neutron capture therapy (BNCT) is a binary radiotherapy that selectively kills tumor cells. Its clinical implementation relies on Mo...

Beam geometry optimization and convergence mode evaluation for knowledge-based planning of hippocampal avoidance whole-brain radiotherapy: A cross-platform study with quantitative scorecard assessment.

BACKGROUND: Hippocampal avoidance whole-brain radiotherapy (HA-WBRT) is used to treat brain metastases while preserving cognitive function by sparing ...

Non-invasive differentiation of diffuse midline glioma and midline glioblastoma using DCE-MRI perfusion parameters and machine learning classification in pediatric and young adult patients.

PURPOSE: Diffuse midline glioma (DMG) and midline glioblastoma (mGBM) are aggressive WHO grade 4 tumors with comparable median survival of 12-18 month...

Fast and robust q-space trajectory imaging using voxel-wise multilayer perceptrons for parameter estimation.

Q-space trajectory imaging (QTI) provides promising markers of tissue microstructure, but clinical translation requires shorter acquisitions, faster a...

From metabolite mapping to precision imaging: advances in in vivo MR spectroscopy of brain tumours.

Magnetic resonance spectroscopy (MRS) provides non-invasive in vivo assessment of brain tumour metabolism and complements conventional magnetic resona...

Deep Learning Algorithm-Reconstructed Triple-Rule-Out CT: Image Quality and Performance in Evaluating Coronary Lumen Stenosis.

To assess radiation burden and contrast medium load, image quality, and the performance of AI-reader and junior radiologists in evaluating coronary st...