AIMC Topic: Brain Neoplasms

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Applications of machine learning to MR imaging of pediatric low-grade gliomas.

Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery
INTRODUCTION: Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading, typing, and segmentation. Moreover, it has been shown that ML can identify cru...

Multicenter privacy-preserving model training for deep learning brain metastases autosegmentation.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
OBJECTIVES: This work aims to explore the impact of multicenter data heterogeneity on deep learning brain metastases (BM) autosegmentation performance, and assess the efficacy of an incremental transfer learning technique, namely learning without for...

Artificial intelligence innovations in neurosurgical oncology: a narrative review.

Journal of neuro-oncology
PURPOSE: Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

Scientific reports
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were rando...

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Scientific reports
Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagn...

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery.

European radiology experimental
Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in ar...

Shape-Scale Co-Awareness Network for 3D Brain Tumor Segmentation.

IEEE transactions on medical imaging
The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are freq...

Radiomics and artificial intelligence applications in pediatric brain tumors.

World journal of pediatrics : WJP
BACKGROUND: The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity an...

Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform.

Journal of computational biology : a journal of computational molecular cell biology
The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN...