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 …

Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors

DR Gutierrez, A Awwad, L Meijer… - American Journal …, 2014 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Qualitative radiologic MR imaging review affords limited
differentiation among types of pediatric posterior fossa brain tumors and cannot detect …

MRI-based whole-tumor radiomics to classify the types of pediatric posterior fossa brain tumor

S Wang, G Wang, W Zhang, J He, W Sun, M Yang… - Neurochirurgie, 2022 - Elsevier
Background Differential diagnosis between medulloblastoma (MB), ependymoma (EP) and
astrocytoma (PA) is important due to differing medical treatment strategies and predicted …

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 …

Ependymoma and pilocytic astrocytoma: Differentiation using radiomics approach based on machine learning

M Li, H Wang, Z Shang, Z Yang, Y Zhang… - Journal of Clinical …, 2020 - Elsevier
Mandatory accurate and specific diagnosis demands have brought about increased
challenges for radiologists in pediatric posterior fossa tumor prediction and prognosis. With …

[HTML][HTML] Machine learning decision tree models for differentiation of posterior fossa tumors using diffusion histogram analysis and structural MRI findings

S Payabvash, M Aboian, T Tihan, S Cha - Frontiers in Oncology, 2020 - frontiersin.org
We applied machine learning algorithms for differentiation of posterior fossa tumors using
apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of …

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T

F Citak-Er, Z Firat, I Kovanlikaya, U Ture… - Computers in biology and …, 2018 - Elsevier
Objective The objective of this study was to assess the contribution of multi-parametric (mp)
magnetic resonance imaging (MRI) quantitative features in the machine learning-based …

MR imaging–based radiomic signatures of distinct molecular subgroups of medulloblastoma

M Iv, M Zhou, K Shpanskaya… - American Journal …, 2019 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Distinct molecular subgroups of pediatric
medulloblastoma confer important differences in prognosis and therapy. Currently, tissue …

Radiomics signature for the prediction of progression-free survival and radiotherapeutic benefits in pediatric medulloblastoma

Z Liu, H Zhang, M Ge, X Hao, X An, Y Tian - Child's Nervous System, 2022 - Springer
Purpose To develop and validate a radiomics signature for progression-free survival (PFS)
and radiotherapeutic benefits in pediatric medulloblastoma. Materials and methods We …

Differentiation between ependymoma and medulloblastoma in children with radiomics approach

J Dong, L Li, S Liang, S Zhao, B Zhang, Y Meng… - Academic radiology, 2021 - Elsevier
Rationale and Objectives Ependymoma (EP) and medulloblastoma (MB) of children are
similar in age, location, manifestations and symptoms. Therefore, it is difficult to differentiate …