Brain Cancer

Unimodal vs. multimodal deep learning for non-invasive MGMT promoter methylation prediction in glioblastoma: A systematic evaluation on the BraTS 2021 dataset.

Glioblastoma multiforme (GBM) is the most aggressive primary brain tumor in adults, with a median survival of 14.6 months under standard radiotherapy and temozolomide (TMZ) chemotherapy. The methylation status of the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter is a critical biomarker predicting TMZ response; however, its determination currently requires invasive tissue sampling. Non-inv...

Deep learning applications in cancer treatment Prediction: Comprehensive research foundation for systematic review and Meta-Analysis.

Deep learning has rapidly emerged as a transformative technology in oncology, offering new capabilities in treatment response prediction and personalized cancer care. This systematic review and meta-analysis aim to evaluate the predictive performance, methodological quality, and clinical implementation of deep learning models for cancer treatment outcomes. A comprehensive search across ten databas...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

T2Pdecoder enables protein-centric analyses from transcriptomic data.

Protein quantification is not as extensive as RNA quantification, especially for isocitrate dehydrogenase (IDH) mutant gliomas. Predicting protein abu...

Pre-operative MRI-Based Radiomics for Predicting Telomerase Reverse Transcriptase Promoter Mutation Status in Glioma Patients: A Systematic Review and Meta-analysis.

TERT promoter (TERTp) mutations shape glioma prognosis and therapy, yet tissue testing can be limited by sampling error and surgical inaccessibility. ...

Simulation-Enhanced Learning in Nuclear Medicine: Theory, Modalities, and Applications Across the Training Continuum.

Simulation-based education has evolved into a foundational component of nuclear medicine technologist training, driven by increasing procedural comple...

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 ...

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 n...

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...