Brain Cancer

Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Glioblastoma Patients.

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, ...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked diffe...

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 Ima...

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 glycolyti...

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 staff...

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 ...

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 Computation...

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...

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 planni...

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 ...

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 derma...

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 p...

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 survi...

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 ai...

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 i...

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 resist...

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 radiatio...

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 ...

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,...

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-ind...

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