Brain Cancer

Artificial intelligence in diagnostic, prognostic, and predictive genomic biomarkers for prostate cancer: Ready for prime time?

INTRODUCTION: Until recently, the widespread use of genetic markers in prostate cancer (PCa) has bee...

Anomaly detection in gamma-ray spectra using autoencoders with small form factor CZT detectors.

The detection of weak radioactive sources in fluctuating background environments is a critical task ...

Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

PURPOSE: To develop and validate a multimodal ensemble machine learning model integrating multi-sequ...

Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.

BACKGROUND: Glioblastoma (GBM) is a highly prevalent and aggressive type of brain tumor characterize...

Metal-based nanomaterials achieve tumor treatment strategies by overcoming the blood-brain barrier of glioma and responding to the tumor microenvironment.

Glioblastoma (GBM) continues to be the most lethal form of primary brain tumor. Therapeutic efficacy...

Automated Lymph Node and Extranodal Extension Assessment Improves Risk Stratification in Oropharyngeal Carcinoma.

PURPOSE: Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be ...

Predicting molecular types of adult-type diffuse gliomas based on MRI reports with large language models.

OBJECTIVES: To evaluate the performance of large language models (LLMs) in predicting molecular type...

The actual performance of ML/AI models in predicting radiation-induced toxicity in head and neck cancer: a systematic review and meta-analysis.

An increasing number of Artificial intelligence (AI) and machine learning (ML) models are being deve...

BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.

This study presents BRAIN-META, a reproducible deep learning methodology designed for multi-class br...

Development of the model for predicting the efficacy of bevacizumab and prognosis in ovarian cancer using deep learning-based pathomics signatures.

Regional-aware and sequence-informed multi-decoder network for robust brain glioma segmentation in multi-parametric MRI.

Accurate segmentation of glioblastoma subregions from multi-parametric MRI is essential for diagnosi...

Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group.

BACKGROUND: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for sever...

Development of a predictive model for Ki-67 index of meningiomas by integrating deep-learning, radiomics and clinical features utilizing fully automated segmentation results.

PURPOSE: To investigate the efficacy of clinical information, traditional radiological, radiomics an...

Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.

Accurate prediction of IDH mutation status in gliomas is critical for guiding diagnosis, prognosis, ...

Establishment of CT diagnostic reference levels (DRLs) in Tokyo.

OBJECTIVE: Diagnostic reference levels (DRLs) are essential for optimizing radiation dose in CT exam...

Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis.

PURPOSE: Stereotactic radiosurgery (SRS) is a standard treatment for brain metastases; however, it m...

Low-dose CBCT image reconstruction: a review.

Cone-beam computed tomography (CBCT) is a critical imaging modality in various medical fields, yet i...

Advancing CNS tumor diagnostics with expanded DNA methylation-based classification.

DNA methylation-based classification is now central to contemporary neuro-oncology, as highlighted b...

Prediction of IDH-mutation and 1p19q-codeletion status in adult-type diffuse gliomas using three-class radiomics models.

PURPOSE: To predict the genetic subtypes of adult-type diffuse gliomas with three-class MRI radiomic...

3D Automated Segmentation of Bronchial Abnormalities on Ultrashort Echo Time MRI: A Quantitative MR Outcome in Cystic Fibrosis.

BACKGROUND: Cystic fibrosis (CF) monitoring relies on computed tomography (CT), but ultra-short echo...

Three-dimensional surface scanning for registration in stereotactic neurosurgery: a cadaveric feasibility study.

OBJECTIVE: Achieving submillimetric accuracy in stereotactic neurosurgery remains critical for safel...