AIMC Topic: Brain Neoplasms

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DEEP Q-NAS: A new algorithm based on neural architecture search and reinforcement learning for brain tumor identification from MRI.

Computers in biology and medicine
A significant obstacle in brain tumor treatment planning is determining the tumor's actual size. Magnetic resonance imaging (MRI) is one of the first-line brain tumor diagnosis. It takes a lot of effort and mostly depends on the operator's experience...

Hybrid classical and quantum computing for enhanced glioma tumor classification using TCGA data.

Scientific reports
Gliomas are the most prevalent malignant primary brain tumors and present diagnostic challenges due to varying survival rates and treatment responses between low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification is crucial f...

Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging.

Scientific reports
Brain tumor segmentation plays a crucial role in clinical diagnostics and treatment planning, yet accurate and efficient segmentation remains a significant challenge due to complex tumor structures and variations in imaging modalities. Multi-feature ...

Predicting the molecular subtypes of 2021 WHO grade 4 glioma by a multiparametric MRI-based machine learning model.

BMC cancer
BACKGROUND: Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment.

A comprehensive analysis of transcription factors identified TCF3 as a prognostic target for glioma.

Scientific reports
Transcription factors (TFs) are pivotal in tumor initiation and progression, regulating downstream gene expression and modulating cellular processes. In this study, we conducted a comprehensive analysis of TF gene sets to define the molecular subtype...

Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture.

Scientific reports
The brain tumours originate in the brain or its surrounding structures, such as the pituitary and pineal glands, and can be benign or malignant. While benign tumours may grow into neighbouring tissues, metastatic tumours occur when cancer from other ...

Integrated clinicopathological-radiomic-blood model for glioma survival prediction via machine learning: a multicenter cohort study.

Neurosurgical review
BACKGROUND: Glioma is characterized by a poor prognosis and limited possibilities for treatment. Previous studies have developed prediction models for glioma using genetic, clinical, pathological, imaging and other aspects; however, few studies have ...

A deep learning-based clinical decision support system for glioma grading using ensemble learning and knowledge distillation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Gliomas are the most common malignant primary brain tumors, and grading their severity, particularly the diagnosis of low-grade gliomas, remains a challenging task for clinicians and radiologists. With advancements in deep learning and medical image ...

Comprehensive multi-omics and machine learning framework for glioma subtyping and precision therapeutics.

Scientific reports
Glioma is a highly heterogeneous and aggressive brain tumour that demands an integrated understanding of its molecular and immunological landscape. We collected multi-omics data from 575 TCGA diffuse-glioma patients (156 IDH-wild-type WHO-grade 4 gli...

Improved salp swarm algorithm-driven deep CNN for brain tumor analysis.

Scientific reports
The efficiency of the swarm-based approach depends on the perfect balance of operators: exploration and exploitation. Due to a lack of balance between these two factors, the Salp Swarm Algorithm (SSA), a recently developed swarm-based metaheuristic a...