Brain Cancer

Deep learning and iterative image reconstruction for head CT: Impact on image quality and radiation dose reduction-Comparative study.

This study focuses on an objective evaluation of a novel reconstruction algorithm-Deep Learning Image Reconstruction (DLIR)-ability to improve image quality and reduce radiation dose compared to the established standard of Adaptive Statistical Iterative Reconstruction-V (ASIR-V), in unenhanced head computed tomography (CT). A retrospective analysis of 163 consecutive unenhanced head CTs was cond...

Construction and Multi-dimensional Validation of a Lactylation-Related Signature for Glioblastoma Multiforme Prognostic and Therapeutic Purposes.

Glioblastoma multiforme, one of the most malignant types of brain tumor, heavily relies on glycolytic pathways and is significantly influenced by immune infiltration and its surrounding microenvironment. Growing evidence implies that increase in glycolysis can lead to lactate accumulation, which further contributed to histone lactylation, playing a crucial role in tumor development, maintenance, a...

Shining the Beam on the Next Generation: A Program Evaluation of a National Workshop Focusing on Medical Student Engagement in Radiation Oncology.

The rising cancer incidence has increased demand for radiation oncologists, surpassing current staffing expansion estimates. Enhancing radiation oncol...

Process management method in head and neck cancer patient care.

Management of patients with head and neck cancer (HNC) is a complex process that involves extensive knowledge on (at least) the discipline of surgery,...

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) pl...

Multimodal MRI radiomics enhances epilepsy prediction in pediatric low-grade glioma patients.

BACKGROUND: Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperativ...

Artificial intelligence in neuro-oncology: methodological bases, practical applications and ethical and regulatory issues.

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches...

Assessment of contour accuracy in head and neck replanning: Deep learning trained model compared with deformable image registration propagation technique.

Accurate contouring is crucial for optimal treatment outcomes, whether for nonadaptive radiotherapy with single images or adaptive radiotherapy (ART) ...

Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy.

PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasophary...

Random field image representations speed up binary discrimination of brain scans and estimate a phenotype glioblastoma cancer cell model.

MRI brain scans alone are not a definitive measure of dementia. Deep-learning algorithms (DLA) and professional human opinion are necessary for diagno...

Synthesizing [F]PSMA-1007 PET bone images from CT images with GAN for early detection of prostate cancer bone metastases: a pilot validation study.

BACKGROUND: [F]FDG PET/CT scan combined with [F]PSMA-1007 PET/CT scan is commonly conducted for detecting bone metastases in prostate cancer (PCa). Ho...

Radiomics and AI-Based Prediction of MGMT Methylation Status in Glioblastoma Using Multiparametric MRI: A Hybrid Feature Weighting Approach.

: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methy...

Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: a multicenter study.

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study aimed to construct and validate a radiomics-based ma...

Trading off Iodine and Radiation Dose in Coronary Computed Tomography.

Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player in the non-invasive assessment of coronary artery d...

Predicting Treatment Outcomes in Glioblastoma: A Risk Score Model for TMZ Resistance and Immune Checkpoint Inhibition.

Glioblastoma (GBM) presents significant therapeutic challenges due to its invasive nature and resistance to standard chemotherapy, i.e., temozolomide ...

Non-orthogonal kV imaging guided patient position verification in non-coplanar radiation therapy with dataset-free implicit neural representation.

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, p...

Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.

OBJECTIVES: Understanding the impact of epilepsy on pediatric brain tumors is crucial to diagnostic precision and optimal treatment selection. This st...

Preoperative Differentiation of Spinal Schwannoma and Meningioma Using Machine Learning-Based Models: A Systematic Review and Meta-Analysis.

BACKGROUND: Regarding the differences in surgical approaches for spinal schwannomas and meningiomas, preoperative differentiation of spinal schwannoma...

Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development.

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, w...

Correction to: Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT‑methylation status, age and sex.