Radiomics of liver MRI predict metastases in mice View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05-28

AUTHORS

Anton S. Becker, Marcel A. Schneider, Moritz C. Wurnig, Matthias Wagner, Pierre A. Clavien, Andreas Boss

ABSTRACT

Background: The purpose of this study was to investigate whether any texture features show a correlation with intrahepatic tumor growth before the metastasis is visible to the human eye. Methods: Eight male C57BL6 mice (age 8-10 weeks) were injected intraportally with syngeneic MC-38 colon cancer cells and two mice were injected with phosphate-buffered saline (sham controls). Small animal magnetic resonance imaging (MRI) at 4.7 T was performed at baseline and days 4, 8, 12, 16, and 20 after injection applying a T2-weighted spin-echo sequence. Texture analysis was performed on the images yielding 32 texture features derived from histogram, gray-level co-occurrence matrix, gray-level run-length matrix, and gray-level size-zone matrix. The features were examined with a linear regression model/Pearson correlation test and hierarchical cluster analysis. From each cluster, the feature with the lowest variance was selected. Results: Tumors were visible on MRI after 20 days. Eighteen features from histogram and the gray-level-matrices exhibited statistically significant correlations before day 20 in the experiment group, but not in the control animals. Cluster analysis revealed three distinct clusters of independent features. The features with the lowest variance were Energy, Short Run Emphasis, and Gray Level Non-Uniformity. Conclusions: Texture features may quantitatively detect liver metastases before they become visually detectable by the radiologist. More... »

PAGES

11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s41747-018-0044-7

DOI

http://dx.doi.org/10.1186/s41747-018-0044-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104245332

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29882527


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