Deep learning for pediatric posterior fossa tumor detection and classification: a multi-institutional study

JL Quon, W Bala, LC Chen, J Wright… - American Journal …, 2020 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric
brain tumors. MR imaging is key to tumor detection, diagnosis, and therapy guidance. We …

Automatic machine learning to differentiate pediatric posterior fossa tumors on routine MR imaging

H Zhou, R Hu, O Tang, C Hu, L Tang… - American Journal …, 2020 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Differentiating the types of pediatric posterior fossa tumors
on routine imaging may help in preoperative evaluation and guide surgical resection …

[HTML][HTML] Deep learning-based studies on pediatric brain tumors imaging: narrative review of techniques and challenges

H Shaari, J Kevrić, S Jukić, L Bešić, D Jokić, N Ahmed… - Brain Sciences, 2021 - mdpi.com
Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic,
and functional changes arising in the brain and non-specific or conflicting imaging results …

MRI-based end-to-end pediatric low-grade glioma segmentation and classification

P Vafaeikia, MW Wagner, C Hawkins… - Canadian …, 2024 - journals.sagepub.com
Purpose: MRI-based radiomics models can predict genetic markers in pediatric low-grade
glioma (pLGG). These models usually require tumour segmentation, which is tedious and …

Development and validation of a deep learning model for brain tumor diagnosis and classification using magnetic resonance imaging

P Gao, W Shan, Y Guo, Y Wang, R Sun, J Cai… - JAMA Network …, 2022 - jamanetwork.com
Importance Deep learning may be able to use patient magnetic resonance imaging (MRI)
data to aid in brain tumor classification and diagnosis. Objective To develop and clinically …

[HTML][HTML] CoMB-deep: composite deep learning-based pipeline for classifying childhood medulloblastoma and its classes

O Attallah - Frontiers in neuroinformatics, 2021 - frontiersin.org
Childhood medulloblastoma (MB) is a threatening malignant tumor affecting children all over
the globe. It is the foremost common pediatric brain tumor causing death. The early and …

Automated tumor segmentation and brain tissue extraction from multiparametric MRI of pediatric brain tumors: A multi-institutional study

A Fathi Kazerooni, S Arif, R Madhogarhia… - Neuro-Oncology …, 2023 - academic.oup.com
Background Brain tumors are the most common solid tumors and the leading cause of
cancer-related death among all childhood cancers. Tumor segmentation is essential in …

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors

J Peng, DD Kim, JB Patel, X Zeng, J Huang… - Neuro …, 2022 - academic.oup.com
Background Longitudinal measurement of tumor burden with magnetic resonance imaging
(MRI) is an essential component of response assessment in pediatric brain tumors. We …

[HTML][HTML] TumorDetNet: A unified deep learning model for brain tumor detection and classification

N Ullah, A Javed, A Alhazmi, SM Hasnain, A Tahir… - Plos one, 2023 - journals.plos.org
Accurate diagnosis of the brain tumor type at an earlier stage is crucial for the treatment
process and helps to save the lives of a large number of people worldwide. Because they …

Deep radiomics for brain tumor detection and classification from multi-sequence MRI

S Banerjee, S Mitra, F Masulli, S Rovetta - arXiv preprint arXiv:1903.09240, 2019 - arxiv.org
Glioma constitutes 80% of malignant primary brain tumors and is usually classified as HGG
and LGG. The LGG tumors are less aggressive, with slower growth rate as compared to …