Brain Cancer

Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients.

Interleukin-18 has broad immune regulatory functions. Genomic data and enhanced Magnetic Resonance Imaging data related to LGG patients were downloaded from The Cancer Genome Atlas and Cancer Imaging Archive, and the constructed model was externally validated using hospital MRI enhanced images and clinical pathological features. Radiomic feature extraction was performed using "PyRadiomics", featur...

Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.

Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation c...

Machine learning-based forest fire susceptibility mapping of Southern Mizoram, a part of Indo-Burma Biodiversity Hotspot.

Forest fires are a significant global environmental hazard, causing widespread economic losses and ecological damage to natural habitats. Biodiversity...

Innovative technologies and their clinical prospects for early lung cancer screening.

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, due to lacking effective early-stage screening approaches. Im...

Machine learning model for predicting recurrence following intensity-modulated radiation therapy in nasopharyngeal carcinoma.

BACKGROUND: Nasopharyngeal carcinoma (NPC) exhibits unique histopathological characteristics compared to other head and neck cancers. The prognosis of...

Quasi-supervised MR-CT image conversion based on unpaired data.

. In radiotherapy planning, acquiring both magnetic resonance (MR) and computed tomography (CT) images is crucial for comprehensive evaluation and tre...

Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.

OBJECTIVE: WHO grade 4 glioma is the most common primary malignant brain tumor, with a median survival of only 14.6 months. Predicting survival outcom...

Nanoneedles enable spatiotemporal lipidomics of living tissues.

Spatial biology provides high-content diagnostic information by mapping the molecular composition of tissues. However, traditional spatial biology app...

Comparative analysis of pre-transcatheter aortic valve implantation CTA protocols: Optimizing radiation dose and contrast volume.

BACKGROUND: To establish the most effective and safe pre-transcatheter aortic valve implantation (TAVI) CT angiography (CTA) protocol by comparing two...

'Bill': An artificial intelligence (AI) clinical scenario coach for medical radiation science education.

INTRODUCTION: The integration of artificial intelligence (AI) into medical radiation science (MRS) education offers significant potential to enhance s...

Stereotactic Body Radiation Therapy for Primary Renal Cancer and Genetic Markers of Response: A Phase 2 Trial.

Controlled outcome assessment of radiotherapy for primary renal cell carcinoma (RCC) remains limited, particularly regarding its impact on ipsilateral...

3D-MRI brain glioma intelligent segmentation based on improved 3D U-net network.

PURPOSE: To enhance glioma segmentation, a 3D-MRI intelligent glioma segmentation method based on deep learning is introduced. This method offers sign...

Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.

PURPOSE: Lattice radiation therapy (LRT) is a form of spatially fractionated radiation therapy that allows increased total dose delivery aiming for im...

Emerging Trends in Artificial Intelligence in Neuro-Oncology.

PURPOSE OF REVIEW: This article explores the evolving role of artificial intelligence (AI) in neuro-oncology, highlighting its potential to enhance di...

CT-based deep learning model for improved disease-free survival prediction in clinical stage I lung cancer: a real-world multicenter study.

OBJECTIVES: To develop a deep learning (DL) model for predicting disease-free survival (DFS) in clinical stage I lung cancer patients who underwent su...

Artificial intelligence for radiotherapy dose prediction: A comprehensive review.

Patient outcomes are significantly impacted by the effectiveness and quality of radiation treatment planning. Deep learning, a branch of artificial in...

Multiscale-fusion models with genomic, topological and pathomic features to predict response to radiation therapy for non-small cell lung cancer patients.

Artificial intelligence models with biomarkers to predict treatment responses to radiation would be necessary to maximise the treatment outcomes of in...

Monitoring of Early Skin Reactions by Optical Coherence Tomography and Dermatoscopy in Patients Receiving Radiation Therapy for Head and Neck Cancer.

PURPOSE: Patients with head and neck cancer undergoing radiation therapy (RT) may experience pronounced acute skin reactions. We tested whether optica...

Automated Diffusion Analysis for Non-Invasive Prediction of IDH Genotype in WHO Grade 2-3 Gliomas.

BACKGROUND AND PURPOSE: Glioma molecular characterization is essential for risk stratification and treatment planning. Noninvasive imaging biomarkers ...

Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.

Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This ...