Brain Cancer

Low-kVp techniques for radiation dose optimization in abdominopelvic contrast-enhanced CT: a scoping review.

Contrast-enhanced computed tomography (CECT) of the abdomen and pelvis is widely used for diagnostic imaging but contributes substantially to cumulative medical radiation exposure. Low tube voltage (kVp) imaging has gained attention as a practical strategy for radiation dose optimization while maintaining diagnostic image quality.The study aimed to map the current evidence on low-kVp techniques fo...

Domain-specific adaptation for MR image synthesis with text-guided diffusion.

Deep learning in medical imaging is severely constrained by data scarcity. Data synthesis offers a promising solution, but existing generative models have difficulty in restoring pathological texture features when trained on small-scale datasets. To address this, we propose a domain-specific, partition-based parallel text-guided Latent Diffusion Model (LDM) for medical image synthesis. Each LDM op...

Evaluating the VOCORDER device for early disease detection through breath analysis: study protocol for a two-phase clinical study.

INTRODUCTION: The survival rate of patients with life-threatening diseases primarily depends on the speed of diagnosis. Too often, diseases are detect...

Quantifying the AI readiness gap: An international, multidisciplinary assessment of artificial intelligence literacy in the radiation oncology community.

PURPOSE: The rapid integration of artificial intelligence (AI) into imaging-intensive fields like radiation oncology (RO) is transforming the clinical...

The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors.

PURPOSE: Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features of...

Toward Autonomous Histotripsy: Integrating Deep Learning Segmentation With Robotic Control for Glioblastoma.

OBJECTIVE: Glioblastoma multiforme (GBM) is an aggressive brain tumor in which incomplete margin delineation during surgery can contribute to residual...

Predicting Bacterial Radiation Resistance Through Identifying Novel Genomic Features.

Radiation resistance in bacteria is a critical trait with implications for biotechnology, medicine and environmental science. Deinococcus species poss...

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.

BACKGROUND: Therapeutic decisions in clinical oncology are commonly established through interdisciplinary consensus in multidisciplinary cancer confer...

Beyond Broca and Wernicke: Epilepsy surgery in the language areas.

Epilepsy surgery in language areas is challenged by the intricacies of presurgical workup and surgical planning. In recent decades, the view of langua...

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the de...

Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.

The molecular and spatial heterogeneity of gliomas severely limits accurate prediction of postoperative adjuvant chemotherapy efficacy, representing a...

Organoids as next-generation models for investigating intracranial tumours.

Tumour organoids have emerged as important tools in brain tumour research, addressing long-standing limitations of conventional two-dimensional cultur...

Proteomic and transcriptomic analyses identify the role of RBM25 in the malignant progression of glioma.

Glioma is a primary tumor derived from central nervous system glial cells. RNA binding motif protein 25 (RBM25) has been implicated in glioma progress...

Towards an accessible, centralised, searchable database for AI courses in Europe: the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project.

OBJECTIVE: Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hind...

Assessment system for short-term lower cranial nerve dysfunction following medulla oblongata glioma surgery: risk stratification and optimal surgical strategy.

OBJECTIVE: Medullary gliomas pose significant surgical risks, particularly the risk of postoperative lower cranial nerve (LCN) dysfunction, which prof...

A recurrent interferon, stress, and survival axis identifies a rare malignant programme across glioblastoma single-cell, spatial, and longitudinal cohorts.

Glioblastoma exhibits profound intratumoral heterogeneity and rapid adaptation under stress, complicating durable therapeutic control. Here, a biologi...

Enhancing brain tumor classification with a simplified CNN through hyperparameter optimization.

Brain tumors remain a major public health challenge because of their high mortality rate and the need for timely and accurate diagnosis. Magnetic Reso...

Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.

PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prio...

Radiation dose has no significant impact on CT-based bone mineral density measurements in a large-animal model.

Bone mineral density (BMD) is a biomarker for frailty, and CT-derived radiodensity can be extracted fully automatically as a surrogate. Because these ...

VMAM-NET: A Model Agnostic Meta-Learning Network for Rare De Novo Glioblastoma Diagnosis.

The diagnosis of grade IV brain tumors, such as de novo glioblastoma, has recently attracted a lot of scientific interest in neuroimaging and deep lea...