AIMC Topic: Brain Neoplasms

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Neutrophil Percentage-to-Albumin Ratio as a Novel Prognostic Biomarker in Adult Diffuse Gliomas: Retrospective Study Integrating 3 Machine Learning Models and Cox Regression.

JMIR medical informatics
BACKGROUND: Adult-type diffuse glioma (ADG) is the most common primary malignant tumor of the central nervous system. Its highly invasive nature, marked heterogeneity, and resistance to therapy contribute to a high risk of recurrence and poor prognos...

A Machine Learning-Driven Electrophysiological Platform for Real-Time Tumor-Neural Interaction Analysis and Modulation.

Nature communications
Neural-tumor electrophysiology-marked by pathological membrane potentials and ion channel dysregulation-emerges as actionable targets to curb tumor aggression. Yet, how neural-driven bioelectrical crosstalk dynamically regulates tumors within functio...

Optimizing and evaluating robustness of AI for brain metastasis detection and segmentation via loss functions and multi-dataset training.

Biomedical physics & engineering express
. Accurate detection and segmentation of brain metastases (BM) from MRI are critical for the appropriate management of cancer patients. This study investigates strategies to enhance the robustness of artificial intelligence (AI)-based BM detection an...

Evaluating AI chatbots in neurological function test interpretation for brain tumor surgery.

Neurosurgical review
Neuropsychological assessments are essential for evaluating functional status and guiding surgical planning in patients with brain tumors. However, their complexity may hinder interpretation for patients and junior clinicians. Large language model (L...

Machine learning-based prediction of glioma grading.

PloS one
OBJECTIVE: Gliomas are among the most common and heterogeneous primary tumours of the central nervous system. Accurate grading is essential for treatment planning and prognosis, yet conventional histopathological approaches are limited by subjectivit...

Ratio maps of T1w/T2w MRI signal intensity do not improve deep-learning segmentation of pediatric brain tumors.

PloS one
INTRODUCTION: T1w/T2w ratio mapping, combining voxel-wise signal intensities in T1-weighted (T1w) and T2-weighted (T2w) structural MRI, has been used to investigate cortical architecture in the brain, but has also shown promise in tissue discriminati...

Connectomics in brain tumor surgery: large-scale clinical feasibility and hypothesis-generating tractometry findings.

Journal of neuro-oncology
BACKGROUND: Maximal tumor resection with neurological preservation is central to brain tumor surgery. This study evaluates the integration of an artificial intelligence-based connectomics platform for surgical planning, with exploratory tractometry a...

Predicting the influence of homologous recombination repair deficiency genes on glioma heterogeneity and patient prognosis using multi-omics analysis and machine learning.

PloS one
BACKGROUND: Glioma is the most common malignant tumor of the central nervous system, and homologous recombination deficiency (HRD) may play a crucial role in its progression. Our study aimed to predict the impact of HRD on glioma heterogeneity and pa...

Regional-aware and sequence-informed multi-decoder network for robust brain glioma segmentation in multi-parametric MRI.

Computers in biology and medicine
Accurate segmentation of glioblastoma subregions from multi-parametric MRI is essential for diagnosis, surgical planning, and treatment monitoring in neuro-oncology. However, effective delineation of surrounding non-enhancing FLAIR hyperintensity, no...

MRI quantitative imaging biomarkers in differentiating brain parenchymal tuberculoma and lung cancer brain metastases.

European journal of medical research
BACKGROUND: Brain parenchymal tuberculoma (BT) and brain metastases (BM) originating from lung cancer often exhibit overlapping clinical and imaging features, making accurate differentiation challenging. Current diagnostic approaches remain suboptima...