Brain Cancer

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 radiation levels is crucial. Machine learning models' implementation for prediction of background radiation levels and anomaly detection can bring a revolutionary change in radiation monitoring. Also, emergency evacuation routes with minimum radiation e...

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 (APTw) chemical exchange saturation transfer (CEST) MRI reflects endogenous protein and peptide content, but its clinical and molecular correlates in therapy-naive glioblastoma, IDH-wildtype, remain incompletely understood. METHODS: This retrospective s...

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...

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...

Measuring AI preparedness in health professions education: Evidence from a national survey of medical radiation science students and new graduates.

INTRODUCTION: Artificial intelligence (AI) in medical radiation science (MRS) is increasingly embedded in everyday clinical workflows. As AI systems a...

TOPOS: target organ prediction on scout views for automated CT scan planning.

BACKGROUND: Overscanning is a common issue in CT planning, leading to unnecessary radiation exposure. PURPOSE: To develop a deep learning model to seg...

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.

Glioblastoma multiforme (GBM) is an aggressive primary brain tumour that presents significant treatment challenges due to its complex pathology and he...

Deep learning for detection and automatic visualization of radiation-induced temporal lobe injury in nasopharyngeal carcinoma across endemic and non-endemic areas in China.

PURPOSE: Detection of radiation-induced temporal lobe injury (RTLI) at the earliest radiologically detectable stage is important for timely interventi...

The Integration of Artificial Intelligence in Radiation Medical Physics: Insights from an International Survey with Regional Variability.

BACKGROUND: Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving ...

Personalized machine learning-guided radiation dose escalation in newly diagnosed glioblastoma: prospective pilot study.

Glioblastoma is a highly aggressive primary brain tumor with near-universal recurrence despite maximal safe resection followed by standard chemoradiat...

Single-cell and deep learning identify hypoxia-responsive lncRNAs predicting outcomes in colorectal cancer.

Emerging evidence highlights hypoxia-responsive long non-coding RNAs (lncRNAs) as potential modulators in tumor biology. In this study, we explored th...

Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.

Glioma is the most common primary brain tumor, with high-grade glioma (HGG) posing significant clinical challenges due to its poor survival outcomes. ...

Application of a multimodal MRI model integrating radiomics and habitat features for predicting glioma pathology and prognosis.

BACKGROUND: Accurate grading and prognostic assessment of glioma requires integrating key molecular biomarkers, including IDH mutation status and the ...

Copper transporters promote the malignant progression of glioblastoma by regulating the immunosuppressive tumour microenvironment via the SLC31A1-WNT5A axis.

BACKGROUND: The prognosis for patients with glioblastoma (GBM) remains extremely poor, a challenge largely attributable to the complex nature of its m...