Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-03

AUTHORS

Nicholas Czarnek, Kal Clark, Katherine B. Peters, Maciej A. Mazurowski

ABSTRACT

In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient's Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20-0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients. More... »

PAGES

55-62

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11060-016-2359-7

DOI

http://dx.doi.org/10.1007/s11060-016-2359-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1042316990

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28074320


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Download the RDF metadata as:  json-ld nt turtle xml License info

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11060-016-2359-7'


 

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