Graph-Radiomics Learning (GrRAiL): Application to Distinguishing Glioblastoma Recurrence from Pseudo-Progression on Structural MRI-Reference-Cited by-同舟云学术

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Graph-Radiomics Learning (GrRAiL): Application to Distinguishing Glioblastoma Recurrence from Pseudo-Progression on Structural MRI

Published:2024-05-27 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Symposium on Biomedical Imaging (ISBI)
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Author:

Battalapalli Dheerendranath¹,Safai Apoorva¹,Ismail Marwa¹,Hill Virginia²,Statsevych Volodymyr²,Huang Raymond³,Ahluwalia Manmeet Singh⁴,Tiwari Pallavi¹

Affiliation:

1. University of Wisconsin-Madison,Department of Radiology,Madison,USA

2. Imaging Institute, Cleveland Clinic,Department of Neuroradiology,Cleveland,USA

3. Harvard Medical School,Department of Radiology,Boston,USA

4. Miami Cancer Institute, Baptist Health South Florida,Miami,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10635099/10635102/10635456.pdf?arnumber=10635456

Reference12 articles.

1. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014–2018

2. RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment

3. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI

4. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

5. Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study

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