Online citations, reference lists, and bibliographies.
← Back to Search

Radiogenomics Of Lower-grade Glioma: Algorithmically-assessed Tumor Shape Is Associated With Tumor Genomic Subtypes And Patient Outcomes In A Multi-institutional Study With The Cancer Genome Atlas Data

M. Mazurowski, K. Clark, Nicholas M. Czarnek, Parisa Shamsesfandabadi, K. Peters, Ashirbani Saha
Published 2017 · Biology, Medicine

Cite This
Download PDF
Analyze on Scholarcy
Share
Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient outcomes using algorithmic measurements of tumor shape in magnetic resonance imaging (MRI). We analyzed preoperative imaging and genomic subtype data from 110 patients with lower-grade gliomas (WHO grade II and III) from The Cancer Genome Atlas. Computer algorithms were applied to analyze the imaging data and provided five quantitative measurements of tumor shape in two and three dimensions. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. Patient outcomes were quantified by overall survival. We found that there is a strong association between angular standard deviation (ASD), which measures irregularity of the tumor boundary, and the IDH-1p/19q subtype (p < 0.0017), RNASeq cluster (p < 0.0002), DNA copy number cluster (p < 0.001), and the cluster of clusters (p < 0.0002). The RNASeq cluster was also associated with bounding ellipsoid volume ratio (p < 0.0005). Tumors in the IDH wild type cluster and R2 RNASeq cluster which are associated with much poorer outcomes generally had higher ASD reflecting more irregular shape. ASD also showed association with patient overall survival (p = 0.006). Shape features in MRI were strongly associated with genomic subtypes and patient outcomes in lower-grade glioma.
This paper references
10.1200/JCO.2005.05.2399
Diffusely infiltrative low-grade gliomas in adults.
F. Lang (2006)
10.1117/12.2217084
Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype
Nicholas M. Czarnek (2016)
10.1186/s40880-015-0071-1
Genomic profiling of lower-grade gliomas uncovers cohesive disease groups: implications for diagnosis and treatment
Changming Zhang (2016)
10.1118/1.597707
Quantitative classification of breast tumors in digitized mammograms.
S. Pohlman (1996)
10.1148/radiol.14132641
Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.
M. Mazurowski (2014)
10.2214/AJR.11.7824
Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape.
S. Yamamoto (2012)
10.1056/NEJMoa1402121
Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.
D. Brat (2015)
10.1093/neuonc/not229
Where are we now? And where are we going? A report from the Accelerate Brain Cancer Cure (ABC2) low-grade glioma research workshop.
J. Huse (2014)
10.1007/s11060-016-2359-7
Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study
Nicholas M. Czarnek (2016)
10.18632/oncotarget.8046
Hypoxia-associated factor expression in low-grade and anaplastic gliomas: a marker of poor outcome.
A. Tchoghandjian (2016)
10.1002/jmri.24879
Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms
L. Grimm (2015)
10.1016/0304-3835(94)90103-1
Computerized characterization of mammographic masses: analysis of spiculation.
M. Giger (1994)
10.1148/radiol.15142698
Breast Cancer: Radiogenomic Biomarker Reveals Associations among Dynamic Contrast-enhanced MR Imaging, Long Noncoding RNA, and Metastasis.
S. Yamamoto (2015)
10.1016/j.jacr.2015.04.019
Radiogenomics: what it is and why it is important.
M. Mazurowski (2015)
10.1016/0041-5553(80)90061-0
Polynomial algorithms in linear programming
L. Khachiyan (1980)
10.1016/j.artmed.2007.06.004
Multi-scaled morphological features for the characterization of mammographic masses using statistical classification schemes
H. Georgiou (2007)
10.1117/12.2255579
Radiogenomic analysis of lower grade glioma: a pilot multi-institutional study shows an association between quantitative image features and tumor genomics
M. Mazurowski (2017)
10.1371/journal.pone.0025451
Radiogenomic Mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme
P. Zinn (2011)
MINIMUM VOLUME ENCLOSING ELLIPSOIDS
N. Moshtagh (2005)
10.1148/radiol.14131375
Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
A. Ashraf (2014)
10.18632/oncotarget.9006
Mutant IDH1 expression is associated with down-regulation of monocarboxylate transporters
Pavithra Viswanath (2016)
10.1097/01.COT.0000289242.47980.F9
Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma
R. Stupp (2005)
10.1148/radiol.13120118
MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set.
D. Gutman (2013)
10.1007/s00401-007-0243-4
The 2007 WHO Classification of Tumours of the Central Nervous System
D. N. Louis (2007)
Aurora TD et al (2013) MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma
DA Gutman (2013)
Hypoxia-associated factor expression in lowgrade and anaplastic gliomas: a marker of poor outcome. Oncotarget
A Tchoghandjian (2016)
10.1056/NEJMoa1407279
Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors.
J. Eckel-Passow (2015)



This paper is referenced by
10.1007/s00261-019-02028-w
Radiogenomics: bridging imaging and genomics
Z. Bodalal (2019)
10.1038/s41598-019-49182-1
Roadmap to Local Tumour Growth: Insights from Cervical Cancer
H. Kubitschke (2019)
10.3390/ijms20236033
TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review
Mario Zanfardino (2019)
10.1016/j.compbiomed.2019.05.002
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Mateusz Buda (2019)
10.1093/noajnl/vdaa044
Radiological differences between subtypes of WHO 2016 grade II–III gliomas: a systematic review and meta-analysis
Djuno I van Lent (2020)
10.1109/TENSYMP50017.2020.9230884
Semantic Segmentation of Self-Supervised Dataset and Medical Images Using Combination of U-Net and Neural Ordinary Differential Equations
Md. Atik Ahamed (2020)
10.3171/2018.10.JNS181290
Intraoperative 3D ultrasound-guided resection of diffuse low-grade gliomas: radiological and clinical results.
H. Bø (2019)
10.1111/ane.12973
Age and surgical outcome of low‐grade glioma in Sweden
A. Corell (2018)
Physically Motivated Feature Development for Machine Learning Applications
Nicholas Czarnek (2017)
10.1109/ICASSP40776.2020.9054030
A Multi-Scaled Receptive Field Learning Approach for Medical Image Segmentation
Pengcheng Guo (2020)
10.1108/lht-08-2019-0173
Comprehensive analysis of potential prognostic biomarker in gliomas
Zun-peng Yu (2020)
10.25777/XGE2-9F89
Model-Based Approach for Diffuse Glioma Classification, Grading, and Patient Survival Prediction
Zeina A. Shboul (2020)
10.1080/14737140.2020.1735367
Advancements in predicting outcomes in patients with glioma: a surgical perspective
A. Jakola (2020)
10.1093/neuonc/noaa177
Fully Automated Hybrid Approach to Predict the IDH Mutation Status of Gliomas via Deep Learning and Radiomics.
Yoon Seong Choi (2020)
10.3892/mmr.2018.9039
G protein‑coupled estrogen receptor/miR‑148a/human leukocyte antigen‑G signaling pathway mediates cell apoptosis of ovarian endometriosis.
S. Z. He (2018)
10.3389/fonc.2019.00374
Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation
A. Chaddad (2019)
10.1007/s00330-019-06561-6
Risk stratification in GIST: shape quantification with CT is a predictive factor
Sheng-cai Wei (2020)
10.1002/jmri.26534
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
M. Mazurowski (2019)
10.1055/s-0040-1715501
Management and Surveillance of Short- and Long-Term Sequelae of Radiation Therapy for the Treatment of Pediatric Brain Tumors
Fred Lam (2020)
10.1016/J.EJRAD.2019.03.003
Modelling MR and clinical features in grade II/III astrocytomas to predict IDH mutation status.
H. Hyare (2019)
10.1016/j.clineuro.2017.12.007
Quantitative texture analysis in the prediction of IDH status in low-grade gliomas
A. Jakola (2018)
10.18632/aging.101594
Radiogenomics of lower-grade gliomas: a radiomic signature as a biological surrogate for survival prediction
Z. Qian (2018)
Semantic Scholar Logo Some data provided by SemanticScholar