Brain Cancer

3D tibial HU reconstruction from biplanar X-rays utilizing a hybrid PCA-CNN framework.

High-resolution Computed Tomography (CT) is the gold standard medical imaging technique for bone assessment. However, its clinical use is limited by high radiation dose (8.8 mSv; biplanar X-rays 1.4 mSv), cost, and reduced accessibility. These barriers are particularly significant for patients requiring frequent imaging. This study introduces a novel hybrid framework combining statistical intensit...

Advancements in radiation dose reduction for pediatric CT head Imaging: A scoping review of emerging Technologies, Protocols, and optimization strategies.

Computed tomography (CT) is an important imaging modality that provides cross-sectional images, aiding in the detailed visualization of internal structures for accurate diagnosis and treatment. The pediatric population is more sensitive to radiation than adults, making radiation dose (RD) optimization an important concern in pediatric CT imaging. This scoping review emphasizes advanced RD reductio...

[Chronic skin damage from UV radiation].

Ultraviolet (UV) radiation is the primary risk factor for the development of both melanocytic and nonmelanocytic skin cancer. In particular, UVA and U...

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 been limited by the complexities and cost of genomic ...

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 for nuclear security, environmental monitoring, an...

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-sequence magnetic resonance imaging (MRI) radiomics, c...

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 characterized by profound molecular complexity and poor progno...

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 is significantly hindered by the presence of the ...

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 diagnosed via surgical pathology. We applied an au...

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 types of adult-type diffuse gliomas according to the 2...

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 developed to predict radiation-induced toxicities (RIT...

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 brain tumor classification using structural MRI. The...

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 diagnosis, surgical planning, and treatment monitoring in ...

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 several primary and metastatic malignancies; however, c...

Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.

PURPOSE: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming because of the...

Circadian rhythm: An emerging force for colorectal cancer treatment.

The relationship between circadian rhythm disorder and the occurrence and progression of colorectal cancer (CRC) has received attention. The circadian...

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 and deep-learning features combinations for construc...

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, and treatment planning. We enrolled 2,537 preopera...

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

OBJECTIVE: Diagnostic reference levels (DRLs) are essential for optimizing radiation dose in CT examinations. However, current DRLs may not reflect th...