2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution
- 22.09.2017
- Research Article
- Verfasst von
- Monika Béresová
- Andrés Larroza
- Estanislao Arana
- József Varga
- László Balkay
- David Moratal
Abstract
Objective
To find structural differences between brain metastases of lung and breast cancer, computing their heterogeneity parameters by means of both 2D and 3D texture analysis (TA).
Materials and methods
Patients with 58 brain metastases from breast (26) and lung cancer (32) were examined by MR imaging. Brain lesions were manually delineated by 2D ROIs on the slices of contrast-enhanced T1-weighted (CET1) images, and local binary patterns (LBP) maps were created from each region. Histogram-based (minimum, maximum, mean, standard deviation, and variance), and co-occurrence matrix-based (contrast, correlation, energy, entropy, and homogeneity) 2D, weighted average of the 2D slices, and true 3D TA were obtained on the CET1 images and LBP maps.
Results
For LBP maps and 2D TA contrast, correlation, energy, and homogeneity were identified as statistically different heterogeneity parameters (SDHPs) between lung and breast metastasis. The weighted 3D TA identified entropy as an additional SDHP. Only two texture indexes (TI) were significantly different with true 3D TA: entropy and energy. All these TIs discriminated between the two tumor types significantly by ROC analysis. For the CET1 images there was no SDHP at all by 3D TA.
Conclusion
Our results indicate that the used textural analysis methods may help with discriminating between brain metastases of different primary tumors.
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- Titel
- 2D and 3D texture analysis to differentiate brain metastases on MR images: proceed with caution
- Verfasst von
-
Monika Béresová
Andrés Larroza
Estanislao Arana
József Varga
László Balkay
David Moratal
- Publikationsdatum
- 22.09.2017
- Verlag
- Springer Berlin Heidelberg
- Erschienen in
-
Magnetic Resonance Materials in Physics, Biology and Medicine
Print ISSN: 0968-5243
Elektronische ISSN: 1352-8661 - DOI
- https://doi.org/10.1007/s10334-017-0653-9
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