Brain Cancer

Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.

OBJECTIVE: Dosiomics and radiomics elaborate the low-and high-order features extracted from images to predict clinical outcomes. Whole-brain radiotherapy (WBRT) has been widely used in patients with diffuse brain metastases of small cell lung cancer (SCLC). The objective of this study is to ascertain the predictors of treatment response in patients with SCLC treated with WBRT. Furthermore, the stu...

Evaluating the impact of deep learning-based image denoising on low-dose CT for lung cancer screening.

PURPOSE: Low-dose CT (LDCT) is increasingly being adopted as a preferred method for lung cancer screening. However, the accompanying rise in image noise necessitates robust denoising strategies. Therefore, this study compared LDCT images with their denoised counterparts using objective image quality metrics and key nodule-related features. METHODS: The dataset utilized in this study was chest CT s...

Machine learning for optimizing mAs in KUB radiography with metal implants.

BACKGROUND AND PURPOSE: Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased...

Radiation-free Cobb angle estimation in adolescent scoliosis using surface topography and a linear regression model.

Scoliosis, the most common spinal deformity in adolescents, requires frequent radiographic follow-up, exposing patients to cumulative ionizing radiati...

Outcome Prediction in Pediatric Traumatic Brain Injury Utilizing Social Determinants of Health and Machine Learning Methods.

BACKGROUND AND PURPOSE: Considerable socioeconomic disparities exist among pediatric patients with traumatic brain injury (TBI). This study aims to an...

Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.

BACKGROUND: Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse condi...

Artificial intelligence-driven 3D surface-topography app for screening and monitoring adolescent scoliosis: early results from a single institution.

PURPOSE: Radiation-free tools, such as scoliometers, ultrasound, and Moiré topography, have been explored for monitoring Adolescent Idiopathic Scolios...

Learnable dendrite neural P systems and applications in survival prediction of glioblastoma patients.

Current neural-like P systems use "point neurons" as the computing entities, and the computations in these neurons are simplified, ignoring the fact t...

Feasibility of single-phase DECT as an alternative to triple-phase CT for imaging-defined risk factors assessment in neuroblastoma for dose reduction.

OBJECTIVE: To investigate the feasibility of evaluating Imaging-Defined Risk Factors (IDRFs) in Neuroblastoma (NB) patients using venous-phase (VP)-on...

Education as a Tool to Navigate Changing Technology in Radiation Oncology.

While technological innovation in radiation therapy (RT) continues to accelerate, safe and equitable adoption of emerging tools is reliant on the read...

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

BACKGROUND: Epidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessm...

Novel "No-Lo" imaging techniques to minimize intraoperative radiation exposure in vascular and endovascular surgery.

INTRODUCTION: The rapid expansion in endovascular techniques has placed vascular surgeons among those most exposed to occupational medical radiation. ...

Seizure risk prediction using machine learning following glioma resection surgery in seizure-naïve patients.

BACKGROUND: Despite the ongoing controversy around the prophylactic use of antiseizure medications (ASMs) in seizure-naïve patients undergoing brain t...

An Efficient 3D Latent Diffusion Model for T1-contrast Enhanced MRI Generation.

Gadolinium-based contrast agents (GBCAs) are commonly employed with T1-weighted (T1w) MRI to enhance lesion visualization but are restricted in patien...

MRI-based multilevel radiomics and transformer features for predicting radiation-induced carotid artery injury after nasopharyngeal carcinoma radiotherapy: A multicenter study.

PURPOSE: To develop and validate an MRI-based fusion model (Rad-SRad-SwinT) integrating conventional radiomics (Rad), subregional radiomics (SRad), an...

Breast cancer frontiers: mapping molecular signals, intelligent diagnostics, and adaptive therapies.

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements in early detection, therapeutic approaches, and c...

ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.

BACKGROUND: Mass spectrometry-based proteomics enables high-throughput quantification of thousands of proteins in clinical samples, fueling biomarker ...

A scoping review of artificial intelligence applications in meningioma from image analysis to prognostic prediction.

Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.

Magnetic resonance imaging (MRI) is central to noninvasive brain tumor assessment, yet clinical uptake of artificial intelligence depends on both accu...

The multi-omics and Mendelian randomization analyses unveiled potential marker genes in the progression of glioblastoma.

In glioma research, identifying key molecules for predicting patient prognosis is challenging due to high heterogeneity. This study explores the corre...