Brain Cancer

Revealing the Infiltration: Prognostic Value of Automated Segmentation of Non-Contrast-Enhancing Tumor in Glioblastoma

Precise delineation of non-contrast-enhancing tumor (nCET) in glioblastoma (GB) is critical for maximal safe resection, yet routine imaging cannot reliably separate infiltrative tumor from vasogenic edema. The aim of this study was to develop and validate an automated method to identify nCET and assess its prognostic value. Pre-operative T2-weighted and FLAIR MRI from 940 patients with newly diagn...

Precision Oncology Through Dialogue: AI-HOPE-RTK-RAS Integrates Clinical and Genomic Insights into RTK-RAS Alterations in Colorectal Cancer

The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hinder...

AI-Powered Segmentation and Prognosis with Missing MRI in Pediatric Brain Tumors

Brain MRI is the main imaging modality for pediatric brain tumors (PBTs); however, incomplete MRI exams are common in pediatric neuro-oncology setting...

Evaluation of Large Language Model-Generated Patient Information for Communicating Radiation Risk

Large language models are increasingly used to generate patient information in healthcare. However, their ability to communicate complex topics, such ...

AI-based Hepatic Steatosis Detection and Integrated Hepatic Assessment from Cardiac CT Attenuation Scans Enhances All-cause Mortality Risk Stratification: A Multi-center Study

Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary art...

Cross-Modal Deep Learning for Integrated Molecular Diagnosis of Cancer: A Prospective Multicenter Study

Accurate integration of histological and molecular features is central to modern cancer diagnostics, but it is often hampered by extended turnaround t...

White matter characterization in regions of edema surrounding meningioma brain tumor using diffusion MRI

White matter (WM) tract detection is critical in presurgical planning of tumor resection however, standard-of-care imaging techniques including T1-wei...

Stacked CNN Architectures for Robust Brain Tumor MRI Classification

Brain tumor classification using MRI scans is crucial for early diagnosis and treatment planning. In this study, we first train a single Convolutional...

Deep learning-based identification of necrosis and microvascular proliferation in adult diffuse gliomas from whole-slide images

For adult diffuse gliomas (ADGs), most grading can be achieved through molecular subtyping, retaining only two key histopathological features for high...

Deep Learning-Assisted Skeletal Muscle Radiation Attenuation at C3 Predicts Survival in Head and Neck Cancer

Head and neck cancer (HNC) patients face an increased risk of malnutrition due to lifestyle, tumor localization, and treatment effects. While skeletal...

Integration of Gene Expression and Digital Histology to Predict Treatment-Specific Responses in Breast Cancer

Deep learning models applied to digital histology can predict gene expression signatures (GES) and offer a low-cost, rapidly available alternative to ...

Optical Microscopy Predictions of Focal Recurrence in Glioblastoma

A hallmark of glioblastoma (GBM) is disease recurrence, which occurs in all patients despite tumor resection, radiation, and chemotherapy. A critical ...

Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb Survivors

The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, ...

Machine Learning-Based Meningioma Location Classification Using Vision Transformers and Transfer Learning

In this study, we aimed to use advanced machine learning (ML) techniques, specifically transfer learning and Vision Transformers (ViTs), to accurately...

Development of a RAG-based Expert LLM for Clinical Support in Radiation Oncology

The ability of pre-trained large language models (LLMs) to rapidly master novel natural language processing tasks holds transformative potential. Howe...

Combined High-Resolution MRSI and [18F]-FACBC PET to Improve the Presurgical Diagnostic Accuracy in Gliomas

Medical imaging is crucial for glioma management. Combined with MRI, amino acid PET may improve glioma diagnosis, biopsy targeting, and tumor delineat...

Thymus Composition, Disease Control, and Toxicity in Locally Advanced Lung Cancer

Thymic involution, characterized by adipose replacement of functional thymic tissue, is a broadly recognized feature of age-related immunosenescence. ...

AI-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning with the Radiation Planning Assistant (RPA)

Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treat...

An Explainable Hybrid CNN–Transformer Framework with Aquila Optimization for MRI-Based Brain Tumor Classification

Accurate and interpretable brain tumor classification remains a critical challenge due to the heterogeneity of tumor types and the complexity of MRI d...

A Glioma Stem Cell–Associated Transcriptomic Program Predicts Survival Across Adult and Pediatric High-Grade Gliomas

High-grade gliomas (HGGs), including adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine gliomas (DIPGs), are sustained by glioma stem ce...