Brain Cancer

DBSCAN Applied to EHRs Data from Patients with Glioblastoma Finds Clusters Based on Cytosolic Hsp70 Protein, Sex, and Brain Subventricular Zone.

Glioblastoma is an aggressive brain cancer that kills approximately one hundred thousand people worldwide every year. Unfortunately, treatment and therapy for patients with this disease are complicated and have limited efficacy in improving individuals' chances of survival. Electronic health records (EHRs) contain patient information collected routinely at hospitals through medical visits and labo...

Generating synthetic CT from MR images for radiation-free computer-assisted quantification of hip morphology.

OBJECTIVES: To compare MR image-based synthetic CT (sCT) with conventional CT for computer-assisted quantification of hip morphology by evaluating osseous structure segmentation quality and hip morphological parameters. MATERIALS AND METHODS: This retrospective study included patients undergoing both CT and MR scans. The sCT images generated from both high-resolution (HR) and simulated low-resolut...

Uncovering G Protein-Coupled Receptors: Novel Targets and Biomarkers for Predicting Glioma Prognosis.

BACKGROUND: Low-grade gliomas (LGG) exhibit significant heterogeneity and recurrence risk. G protein-coupled receptors (GPCR) contribute to glioma mal...

Interpretable machine learning models for bladder cancer overall survival prediction development and external validation via SEER database and Chinese cohort analysis.

OBJECTIVE: We developed interpretable machine learning(ML) models to predict overall survival in bladder cancer patients. This approach aims to improv...

Explainable Deep Learning Framework for Multimodal Brain Tumor Classification via Neuroimaging Attribute Extraction.

Accurate grading of brain tumors from multiparametric MRI is a critical step in treatment planning, yet deep learning models trained for this task rem...

RADIANT: A fully configurable radiotherapy dose prediction framework.

Treatment planning is a multi-disciplinary effort that requires medical decision-making, specialized training, and access to specialized software. Rec...

A neural network embedding the linear-quadratic model for improved prediction of cellular response to ion beam exposure.

Predicting biological responses to ionizing radiation is challenging due to the complex, multi-scale mechanisms involved. Traditional machine learning...

Clinical knowledge constrained multi-task learning framework for breast cancer diagnosis using ultrasound videos.

Ultrasound is widely used in breast cancer diagnosis due to its cost-effectiveness, non-invasiveness, and radiation-free properties. Computer-aided di...

Applications of bioimpedance measurement in clinical and allied fields: a comprehensive review.

BACKGROUND: Accurate and timely disease detection is essential in modern healthcare. Conventional imaging methods such as computed tomography (CT), ma...

Advanced Data Analytics and Machine Learning for Geospatial Interpolation, Anomaly Detection, Background Prediction and Emergency Evacuation Routing in Real-Time Radiation Monitoring System.

For identifying natural trends, hotspots, hazardous areas, and mitigating potential health risk to the public and environment, spatial analysis of rad...

APTw CEST MRI in therapy-naive IDH-wildtype glioblastoma: insights into tumor heterogeneity and molecular subtypes.

PURPOSE: Advanced MRI techniques may provide non-invasive insight into the molecular heterogeneity of glioblastoma. Amide proton transfer-weighted (AP...

MANet: a multimodal attention convolutional neural network for brain tumor classification.

Classification of brain tumors is a difficult problem in medical imaging analysis. Over the past few years, various deep learning-based techniques hav...

Physics-informed DynUNet for brain metastasis segmentation.

BACKGROUND: In neuro-oncology, detecting, segmenting, and delineating the boundaries of small-volume brain metastatic foci remains a significant chall...

Radiation biomarker screening and dose reconstruction based on machine learning.

PURPOSE: Nuclear emergency medical rescue is a critical component of the nuclear emergency response system, playing a vital role in safeguarding publi...

Beyond gross total resection (GTR): Deep peritumoral radiomics for predicting overall survival (OS) and O6-methylguanine-DNA-methyltransferase promoter methylation (MGMTpm) status in glioblastoma multiforme.

BACKGROUND & OBJECTIVE: Glioblastoma Multiforme (GBM) is an aggressive and highly heterogeneous brain tumor with poor survival outcomes. While convent...

Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.

OBJECTIVES: Incomplete MRI sequences pose a significant challenge to the reliability of multiparametric MRI (mp-MRI) radiomics models. This study aime...

T2-FLAIR digital subtraction radiomics versus neuroradiologist visual assessment for differentiating IDH-mutant astrocytomas from other non-enhancing low-grade gliomas: An externally validated machine learning study.

PURPOSE: Non-invasive differentiation of isocitrate dehydrogenase (IDH)-mutant, 1p/19q non-codeleted astrocytomas from other non-enhancing low-grade g...

Artificial intelligence iterative imaging reconstruction with low-dose CT scans for hepatic metastases.

Contrast-enhanced CT is commonly used in the evaluation of hepatic metastatic lesions. This prospective study aimed to assess the capability of artifi...

Digital Twins as the Implementation Layer of Precision Medicine in Pediatric Neurosurgery.

Pediatric neurosurgery increasingly utilizes precision medicine, but practitioners encounter challenges in translating complex data into individualize...

Efficacy of short-course bevacizumab for brain tumor radiation necrosis with a volumetric analysis.

PURPOSE: Radiation necrosis (RN) is a challenging complication of cranial irradiation, often requiring corticosteroids for management. This study eval...