Radiomics of liver MRI predict metastases in mice

被引：36
作者：
Becker A.S. [1 ]
Schneider M.A. [2 ]
Wurnig M.C. [1 ]
Wagner M. [1 ]
Clavien P.A. [2 ]
Boss A. [1 ]
机构：
[1] Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, Zurich
[2] Division of Transplantation and Visceral Surgery, University Hospital Zurich, Raemistrasse 100, Zurich
来源：
European Radiology Experimental | / 2卷 / 1期
关键词：
Colorectal neoplasms; Computer-assisted image processing; Liver; Magnetic resonance imaging (MRI); Neoplasm micrometastases;
D O I：
10.1186/s41747-018-0044-7
中图分类号：
学科分类号：
摘要：
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. © 2018, The Author(s).
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