Brain Cancer

UPhAIR: A Hybrid Pipeline for Generating Understandable Post-hoc AI Reports in Glioma IDH Mutation Status Prediction

Clinical adoption of machine learning (ML) in medical imaging is limited by the lack of interpretability. To address this, we present understandable post-hoc artificial intelligence reports (UPhAIR), a pipeline designed to generate transparent, evidence-based explanations by combining Shapley additive explanation (SHAP) analysis with retrieval-augmented generation (RAG) and large language models (...

Cholesteryl Ester as a Prognostic Biomarker In IDH-wildtype Glioblastoma

Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolomide. Although MGMT methylation is routinely conducted to predict chemosensitivity, its efficacy is often compromised. Thus, there is an urgent need to discover more accurate prognostic biomarkers. Cholesteryl ester (CE) has been recently recognized as a key feature of GBM, however, its role in GBM p...

BRICKS: Compositional Neural Markov Kernels for Zero-Shot Radiation-Matter Simulation

We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics t...

Orientation-Aware Unsupervised Domain Adaptation for Brain Tumor Classification Across Multi-Modal MRI

The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is severely constrained by limited expert-annotated MRI d...

TRACED: In vivo imaging of extracellular intrinsic diffusivity, tortuosity, cell size distribution and cell density in human glioma patients

The lack of analytical models describing diffusion time dependence at intermediate time scales in complex tissue microstructure limits the accurate qu...

Virtual Scanning for NSCLC Histology: Investigating the Discriminatory Power of Synthetic PET

Accurate histological differentiation between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is critical for personalized treatment in non-sma...

Automatic Bevacizumab Response Prediction in Ovarian Cancer from Digital Pathology Images via Novel AI-based Computational Pipeline

Ovarian cancer is one of the gynecological cancer types, which, if metastasized and not detected early, can cause deaths among women. Therefore, there...

Predicting one-year postoperative functional status in contrast-enhancing glioma

Background and Objectives Preoperative prediction of functional outcomes in contrast-enhancing glioma could support surgical decision-making and patie...

A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance

Accurate intraoperative assessment of glioma infiltration is essential for maximizing tumor resection while preserving functional brain tissue. Fluore...

Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery

Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascul...

GlioVision: A Multi-Modal MRI Framework for Non-Invasive Glioma Molecular Biomarkers Prediction

Gliomas are aggressive primary brain tumors that necessitate critical molecular biomarker predictions for optimal clinical decision-making. Traditiona...

Glio-SERS: Label-Free Molecular Profiling of Plasma Extracellular Vesicles in Brain Tumors Using SERS and Artificial Intelligence

Extracellular vesicles are increasingly recognized as important carriers of disease-associated molecular information, yet robust methods for their iso...

an interpretable vision transformer framework for automated brain tumor classification

Brain tumors represent one of the most critical neurological conditions, where early and accurate diagnosis is directly correlated with patient surviv...

AI Approach for MRI-only Full-Spine Vertebral Segmentation and 3D Reconstruction in Paediatric Scoliosis

MRI is preferred over CT in paediatric imaging because it avoids ionising radiation, but its use in spine deformity assessment is largely limited by t...

Topology-Driven Fusion of nnU-Net and MedNeXt for Accurate Brain Tumor Segmentation on Sub-Saharan Africa Dataset

Accurate automatic brain tumor segmentation in Low and Middle-Income (LMIC) countries is challenging due to the lack of defined national imaging proto...

Auxiliary Clinical Prompt Integration into Vision-Language Prompt SAM for Brain Tumor Segmentation

Background. Adult diffuse glioma is a representative class of primary brain tumors for which accurate MRI-based tumor segmentation is indispensable fo...

Multi-Stain Fusion of Histopathology Images Using Deep Learning for Pediatric Brain Tumor Classification

The classification of pediatric brain tumors is investigated using deep learning on hematoxylin and eosin (H&E) and antigen Ki-67 (Ki-67) whole slide ...

A Scalable High-Density Microwell Assay for Single-Cell Clonal Expansion Profiling

Traditional clonogenic assays remain central to evaluating the self-renewal capacity of tumor cells. However, the assay relies on subjective endpoint ...

Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge

Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels a...

Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)

Glioma is a harmful brain tumor that requires early detection to ensure better health results. Early detection of this tumor is key for effective trea...