Brain Cancer

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 possess unique genomic features that enable their extraordinary survival in extreme environments. However, the diversity of radiation resistance mechanisms across bacteria remains poorly explored. Here, we analysed the genomic diversity and functional ge...

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 language-related circuitry has shifted from being localized in a few cortical centers to a distributed, dynamically interconnected system, increasing complexity. In this framework, neuropsychology, functional neuroimaging and neurophysiological assessments...

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...

Artificial intelligence and transforming cancer care.

Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized app...

Super-resolution deep learning reconstruction achieves low-dose high-resolution imaging in pediatric cardiac computed tomography.

BACKGROUND: Cardiac computed tomography (CT) is widely utilized in pediatric cardiology, but minimizing radiation exposure is essential. Recently, sup...

Predicting late radiation-associated neurocognitive and endocrine toxicity in patients with brain tumors.

PURPOSE: Neurocognitive and endocrine dysfunction are potential complications of cranial irradiation. However, risk factors are poorly understood, imp...

Artificial intelligence analysis of temporalis muscle thickness for monitoring sarcopenia and clinical outcomes in individuals with paediatric brain tumours: a retrospective cohort study.

BACKGROUND: People with and who have survived paediatric brain tumour (PBT) have a poor quality of life due to physiological frailty, a primary compon...

DADWMorph: A Global-Local Collaborative Network for Deformable Medical Image Registration.

Deformable image registration enables spatial alignment across sequential scans for longitudinal disease monitoring, multi-modal fusion in treatment p...

Decoding lactylation heterogeneity in glioblastoma: machine learning identifies G6PC3 as a prognostic target.

BACKGROUND: Glioblastoma (GBM) is the most common malignant glioma in adults. It has an extremely poor prognosis, highlighting an urgent need for new ...

Exploring the value of peritumoral brain zone for classification of two malignant brain tumors based on the MRI interpretable models.

OBJECTIVES: This study aims to develop and validate a novel multimodal interpretable artificial intelligence model capable of fusing radiomics feature...