Latest AI and machine learning research in brain cancer for healthcare professionals.
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respirato...
The goal of this cadaver study was to evaluate the feasibility and safety of da Vinci robot-assisted...
In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) a...
PURPOSE: To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) im...
PURPOSEÂ : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. N...
Tissue texture is known to exhibit a heterogeneous or non-stationary nature; therefore using a singl...
Background: Non-invasive diagnosis, reliable recurrence surveillance remain critical unmet needs in ...
Background: Extent of resection remains central to meningioma management, yet Simpson grading is sub...
Tumour-educated platelets (TEPs) carry cancer-type-specific RNA signatures accessible through whole-...
While multimodal data integrating diverse imaging and clinical tabular records is crucial for accura...
Ionizing radiation induces molecular responses that may be used to estimate exposure when physical d...
Background: Meningiomas are the most common primary intracranial tumors in adults, and volumetric as...
Glioblastoma multiforme (GBM) is characterised by profound genomic heterogeneity and heavy-tailed ge...
Background: Previous machine learning models to intraoperatively predict the molecular status of gli...
Background: Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. I...
Single-cell RNA sequencing (scRNA-seq) has transformed our ability to resolve cellular heterogeneity...
Clinical adoption of machine learning (ML) in medical imaging is limited by the lack of interpretabi...
Current treatment of IDH-wildtype glioblastoma (GBM) relies on the first-line chemotherapy-temozolom...
We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a...
The clinical integration of deep learning models for brain tumor diagnosis in neuro-oncology is seve...
The lack of analytical models describing diffusion time dependence at intermediate time scales in co...