Brain Cancer

Performance enhancement of THz antenna with dielectric resonator and prediction bandwidth using machine learning approach for 6G applications.

The need for ultra-low latency and ultra-wideband in 6G applications requires efficient solutions for dielectric resonator antenna design. This paper presents the results of using a machine learning approach to improve the performance of a dielectric resonator antenna operating in the terahertz frequency band. The antenna design uses a polyimide substrate material with a compact size of 58 × 73 µm...

Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality.

To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is non-inferior in image quality to standard-dose CTPA using iterative reconstruction. A phantom study was conducted to estimate the additional radiation dose reduction enabled by high-strength deep learning-based image reconstruction (DLIR-H) compared t...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...

Integrating Molecular Pathology, Tumor Microenvironment, and Novel Therapies to Overcome Resistance in Glioblastoma.

Glioblastoma multiforme (GBM) represents the most aggressive primary brain tumor in adults, characterized by significant heterogeneity, rapid progress...

Radiomic and dosiomic machine learning models for predicting radiation-induced oral mucositis in head and neck cancer: a systematic review.

OBJECTIVE: To critically evaluate machine learning (ML) models developed for predicting radiation-induced oral mucositis (OM) in head and neck cancer ...

Integrated CT-PET radiogenomics and graph-based multi-task learning for preoperative prediction of key glioma molecular markers.

BACKGROUND: The clinical management of glioma is increasingly dependent on the tumor's molecular profile, particularly the mutation status of Isocitra...

Multimodal deep learning integration of clinical, dosiomics, and radiomics features to predict toxicity in breast cancer patients undergoing hypofractionated radiotherapy.

BACKGROUND AND PURPOSE: Radiation dermatitis (RD) and superficial soft tissue fibrosis are common toxicities among the patients with breast cancer rec...

Deep learning for H&E-based meningioma molecular classification and outcome prediction: a retrospective cohort study.

BACKGROUND: The introduction of genomic profiling as a tool for molecular classification and clinical outcome prediction has revolutionised the care o...

Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.

Gliomas are heterogeneous primary central nervous system (CNS) tumors with diverse molecular subtypes and variable prognosis. A paradigm shift in hist...

Understanding resistance in glioblastoma: insights into personalized and targeted therapeutic strategies.

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant brain tumor. Despite combined treatments, including surgical removal...

Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks.

This study presents the wavelet-based physics-informed neural networks (PINNs) simulation to analyse entropy generation in hybrid nanofluid peristalti...

Radiation dose-aware sinogram knowledge library transformer with feature modulation for low-dose medical image segmentation.

Low-dose computed tomography (LDCT) and low-dose positron emission tomography (LDPET) enable shorter acquisition times and lower radiation exposure. H...

CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53).

PURPOSE: Molecular subtyping guides diagnosis and targeted therapy for gliomas. Although MRI-the current imaging standard-can be time-consuming and is...

Federated MobileNetV2 with ensemble meta-learning for privacy-preserving brain tumor classification.

The identification of brain tumors from MRI images is very crucial for the selection of an appropriate treatment. However, the existing solution has i...

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulati...

Domain-specific adaptation for MR image synthesis with text-guided diffusion.

Deep learning in medical imaging is severely constrained by data scarcity. Data synthesis offers a promising solution, but existing generative models ...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.

PURPOSE: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

OBJECTIVE: Glioblastoma multiforme (GBM) is an aggressive brain tumor in which incomplete margin delineation during surgery can contribute to residual...