Brain Cancer

Evaluating artificial intelligence-generated synthesized mammography as a standalone alternative to digital mammography with or without tomosynthesis.

PURPOSE: To evaluate whether standalone synthesized mammography (SM) can maintain or improve diagnostic accuracy while reducing reading time and radiation dose, compared to digital breast tomosynthesis (DBT) with digital mammography (DM) or DM alone. MATERIALS AND METHODS: This was a retrospective study. SM images were generated using an AI-supported DBT reading method that synthesizes key feature...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applications in reduced-dose DECT for evaluating multiple myeloma remain unclear. This study aimed to evaluate image quality and osteolytic lesion detectability of reduced-dose DECT with DLIR, compared with routine-dose single-energy CT (SECT) with adaptiv...

Development of a model for design radiation shielding composite aprons using machine learning.

This study presents the development and evaluation of a novel lead-free composite for radiation shielding, designed using an artificial neural network...

Predicting fire consequences with the transformer model based on multimodal feature fusion.

Accurate prediction of fire consequences is fundamental to process safety management and quantitative risk assessment in the chemical process industri...

From data-rich to evidence-ready: A narrative review of generative artificial intelligence as a statistical scaffold in medical radiation sciences research.

BACKGROUND: Medical radiation science (MRS) research faces a growing asymmetry between a small body of high-rigour, statistically robust studies and a...

Habitat-informed MRI Radiomics and Deep Learning Fusion for Short-Term Survival Prediction in Patients with Glioblastoma: Exploratory Radiogenomic and Immune Correlates.

RATIONALE AND OBJECTIVES: To develop and externally validate a preoperative multicontrast MRI stacking model integrating unsupervised habitat radiomic...

Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.

Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Sur...

Unraveling the molecular landscape of chronic radiation injury: From oxidative stress signaling to translational modeling (Review).

The expanding footprint of human radiation exposure, driven by advances in interventional diagnostics, the resurgence of the nuclear industry and the ...

Longitudinal Plasma Proteomics Reveals an Immuno-thrombotic Signature that Predicts Radiation Pneumonitis in Lung Cancer.

PURPOSE: Radiation pneumonitis (RP) is a dose-limiting toxicity in lung cancer radiotherapy, often poorly predicted by static clinical and dosimetric ...

Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.

BACKGROUND: Positron emission tomography (PET) is a key tool for quantitative brain imaging, but its image quality and quantitative reliability are st...

Machine learning based hepatic safety score predicts decompensation in hepatocellular carcinoma systemic therapy.

Hepatocellular carcinoma (HCC) frequently coexists with portal hypertension, significantly increasing the risk of hepatic decompensation (HD) and vari...

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 the...

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 oss...

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...