Brain Cancer

Visual Outcomes after Suprasellar Meningioma Resection: A Retrospective Cohort Study and a Machine Learning-Based Predictive Model.

 In this research, the authors provide a retrospective cohort study of 82 patients with suprasellar meningiomas to identify predictors of the visual outcome following surgery. We also conducted a matched retrospective case-control analysis.  This retrospective cohort study included all patients who underwent craniotomy for surgical excision of suprasellar meningiomas at our institution between J...

An Innovative Inducer of Platelet Production, Isochlorogenic Acid A, Is Uncovered through the Application of Deep Neural Networks.

(1) Background: Radiation-induced thrombocytopenia (RIT) often occurs in cancer patients undergoing radiation therapy, which can result in morbidity and even death. However, a notable deficiency exists in the availability of specific drugs designed for the treatment of RIT. (2) Methods: In our pursuit of new drugs for RIT treatment, we employed three deep learning (DL) algorithms: convolutional ne...

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM).

PURPOSE: Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize ...

Auto-segmentation of Adult-Type Diffuse Gliomas: Comparison of Transfer Learning-Based Convolutional Neural Network Model vs. Radiologists.

Segmentation of glioma is crucial for quantitative brain tumor assessment, to guide therapeutic research and clinical management, but very time-consum...

Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs...

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients.

BACKGROUND CONTEXT: Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these spe...

A Review of deep learning methods for denoising of medical low-dose CT images.

To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing th...

Exploring the role of large language models in radiation emergency response.

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, pop...

Deciphering the fibrotic process: mechanism of chronic radiation skin injury fibrosis.

This review explores the mechanisms of chronic radiation-induced skin injury fibrosis, focusing on the transition from acute radiation damage to a chr...

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BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep lear...

Deep learning-based algorithms for low-dose CT imaging: A review.

The computed tomography (CT) technique is extensively employed as an imaging modality in clinical settings. The radiation dose of CT, however, is sign...

Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights.

Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI...

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus.

Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between...

Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.

In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival ou...

Novel Operation Mechanism and Multifunctional Applications of Bubble Microrobots.

Microrobots have emerged as powerful tools for manipulating particles, cells, and assembling biological tissue structures at the microscale. However, ...

GlioPredictor: a deep learning model for identification of high-risk adult IDH-mutant glioma towards adjuvant treatment planning.

Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment plann...

Added value of dynamic contrast-enhanced MR imaging in deep learning-based prediction of local recurrence in grade 4 adult-type diffuse gliomas patients.

Local recurrences in patients with grade 4 adult-type diffuse gliomas mostly occur within residual non-enhancing T2 hyperintensity areas after surgica...

Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinica...

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Gra...

Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction.

Glioblastoma presents characteristically with an exuberant, poorly functional vasculature that causes malperfusion, hypoxia and necrosis. Despite limi...