Brain Cancer

Machine learning-based prognostic model for local control prediction in lung cancer patients undergoing stereotactic body radiation therapy: a preliminary study.

PURPOSE: Predicting local recurrence after stereotactic body radiation therapy (SBRT) for lung cance...

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 t...

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 scre...

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...

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...

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 t...

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

BACKGROUND: Artificial intelligence, particularly machine learning, has great potential to improve h...

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 exp...

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 Neur...

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

While technological innovation in radiation therapy (RT) continues to accelerate, safe and equitable...

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 glioblast...

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 thos...

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 (...

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...

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...

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

Breast cancer continues to be a significant worldwide health concern, requiring ongoing improvements...

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

BACKGROUND: Mass spectrometry-based proteomics enables high-throughput quantification of thousands o...

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 upta...

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...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening...