Brain Cancer

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 grading methods, which rely on invasive biopsies, are limited by tumor location. In contrast, magnetic resonance imaging (MRI) offers a non-invasive, high-resolution tool with multi-sequence MRI (e.g., T1, T2, T1C) enabling comprehensive tumor asses...

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 dismal (median survival ∼15 months) and current prognostic models often function as "black boxes," lacking interpretability and limiting clinical utility. There is an urgent need for interpretable prognostic tools to better stratify GBM patients. This s...

Interpreting cancer genetics through a two-step "evolutionary cascade hypothesis": bridging neutral and selective perspectives.

BACKGROUND: DNA mutations are the fundamental engines of cancer, driving its initiation and progression. The forces that fuel malignancy are also the ...

Towards real time AI-augmented fluorescence-guided surgery: Evidence and translational readiness across neurosurgical, gynaecological, and thoracic oncology.

Fluorescence-guided surgery (FgS) is increasingly used across oncologic specialties to enhance intraoperative visualisation of tumour tissue and lymph...

The use of advanced machine learning to predict outcomes after atezolizumab plus bevacizumab for advanced hepatocellular carcinoma: a retrospective cohort study.

BACKGROUND: Combination immune checkpoint inhibitors are recommended as first-line therapy for advanced hepatocellular carcinoma. However, only a thir...

Reinforcement learning-guided segment anything model for MRI prostate and dominant intraprostatic lesions auto-segmentation.

Objective. Accurate segmentation of the prostate and dominant intraprostatic lesions (DILs) on magnetic resonance imaging (MRI) is important for prost...

Toward genomic personalization of breast cancer radiotherapy: foundations, challenges, and a roadmap for clinical integration.

Personalizing radiotherapy dose in breast cancer remains a major unmet need, as current treatment paradigms rely on uniform prescriptions that overloo...

Detection of esophageal varices and prediction of hepatic decompensation in unresectable hepatocellular carcinoma using AI.

BACKGROUND & AIMS: In hepatocellular carcinoma (HCC) with cirrhosis, portal hypertension worsens outcomes. Esophagogastroduodenoscopy (EGD), the curre...

Development of a Real-time Radiation Detection System that Uses IoT to Provide Real-time Monitoring in Custom-built Website.

Effective radiation monitoring is crucial for ensuring public security and safety, particularly in the event of nuclear (e.g., nuclear accident, fallo...

Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm.

OBJECTIVES: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm. MATE...

Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.

PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicato...

Machine learning-based radiopathomics ensemble model for predicting multiple molecular subtypes of adult diffuse glioma: a multicenter retrospective study.

Deep Learning Reconstruction Combined with Contrast-Enhancement Boost Technique in "Quadruple-low" CCTA Protocol: Evaluation of Image Quality and Diagnostic Accuracy.

RATIONALE AND OBJECTIVES: To evaluate the impact of a deep learning reconstruction (DLR) algorithm combined with contrast-enhancement boost (CE-boost)...

NIAID Biodosimetry Strategic Plan.

In the face of emerging threats from natural disasters, nuclear accidents, and potential malicious use of radiation, the National Institute of Allergy...

Precision imaging and evolving therapies in paragangliomas and pheochromocytomas: from molecular diagnostics to imaging-guided management.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors originating from neural crest-derived chromaffin tissue, marked by clinica...

Evaluating Sociodemographic Biases in Artificial Intelligence-Based Glioblastoma Response Assessment Algorithms.

BACKGROUND AND PURPOSE: Recent studies have demonstrated bias in various medical imaging artificial intelligence (AI) models, yet the factors underpin...

A microenvironment-determined risk continuum refines subtyping in meningioma and reveals determinants of machine learning-based tumor classification.

Classification of tumors in neuro-oncology today relies on molecular patterns (mostly DNA methylation) and their machine learning-supported interpreta...

Unlocking the full potential of MRI with artificial intelligence for pre-operative meningioma evaluation.

Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.

BACKGROUND: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation expo...

Sequential glioblastoma segmentation via topological data analysis and spatial adjacency.

Segmenting glioblastoma in medical imaging remains challenging due to the tumor's irregular shape, heterogeneous texture, and poorly defined boundarie...