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Abdominal Radiology

, Volume 44, Issue 9, pp 3175–3184 | Cite as

Texture analysis of placental MRI: can it aid in the prenatal diagnosis of placenta accreta spectrum?

  • Eric Chen
  • Winnie A. MarEmail author
  • Jeanne M. Horowitz
  • Amanda Allen
  • Priyanka Jha
  • Donald R. Cantrell
  • Kejia Cai
Pelvis
  • 91 Downloads

Abstract

Purpose

To determine if texture analysis can differentiate placenta accreta spectrum (PAS) from normal placenta on MRI.

Methods

We performed retrospective image analysis of 80 patients, comprised of 46 patients with PAS and 34 patients without PAS. Histopathology was used as the reference standard. Sagittal single shot fast spin echo T2-weighted MRI sequences acquired from a single institution were analyzed. Placental heterogeneity was quantified using in-house software on a Matlab platform, including the standard deviation of pixel intensity, coefficient of variation, gray-level co-occurrence matrices (GLCM), histogram-oriented gradients (HOG), and fractal analysis with box sizes from 2 to 512. Two-tailed unpaired Student’s t test was used with statistical significance of p < 0.05.

Results

PAS was associated with higher values for standard deviation of pixel intensity and fractal analysis at every box size. Fractal analysis at box sizes 256 (p = 0.011) and 32 (p = 0.021), and standard deviation of pixel intensity (p = 0.023) were the most statistically significant. Fractal values at box size 256 for PAS was 0.13 versus 0.090 for patients without PAS, while standard deviation of pixel intensity was 3.7 for PAS versus 2.5 for patients without PAS. No statistically significant association between PAS and GLCM, coefficient of variation, and HOG was found.

Conclusion

Statistically significant differences were found between normal and abnormal groups using standard deviation of pixel intensity and fractal analysis.

Keywords

Placenta accreta spectrum Placenta accreta Texture analysis Fractal analysis MRI 

Notes

Acknowledgements

The authors acknowledge Dr. Kruti P. Maniar, MD for assistance with clarification of pathological criteria in diagnosis of placenta accreta spectrum, and CCTS support for statistical analysis assistance (Grant Number UL1TR002003).

Compliance with ethical statement

Conflict of interest

The authors declare that they have no conflict of interest.

IRB statement

This study was approved by the IRB of the two main test sites, University of Illinois at Chicago and Northwestern.

References

  1. 1.
    Jauniaux E, Ayres-de-Campos D (2018) FIGO consensus guidelines on placenta accreta spectrum disorders: Introduction. International Journal of Gynecology & Obstetrics 140 (3):261-264.  https://doi.org/10.1002/ijgo.12406 CrossRefGoogle Scholar
  2. 2.
    Warshak CR, Eskander R, Hull AD, Scioscia AL, Mattrey RF, Benirschke K, Resnik R (2006) Accuracy of ultrasonography and magnetic resonance imaging in the diagnosis of placenta accreta. Obstet Gynecol 108 (3 Pt 1):573-581.  https://doi.org/10.1097/01.aog.0000233155.62906.6d CrossRefGoogle Scholar
  3. 3.
    Belfort MA, Medicine PCSoMF (2010) Placenta accreta. Am J Obstet Gynecol 203 (5):430-439.  https://doi.org/10.1016/j.ajog.2010.09.013 CrossRefGoogle Scholar
  4. 4.
    Jauniaux E, Collins S, Burton GJ (2018) Placenta accreta spectrum: pathophysiology and evidence-based anatomy for prenatal ultrasound imaging. Am J Obstet Gynecol 218 (1):75-87.  https://doi.org/10.1016/j.ajog.2017.05.067 CrossRefGoogle Scholar
  5. 5.
    Einerson BD, Rodriguez CE, Kennedy AM, Woodward PJ, Donnelly MA, Silver RM (2018) Magnetic resonance imaging is often misleading when used as an adjunct to ultrasound in the management of placenta accreta spectrum disorders. Am J Obstet Gynecol 218 (6):618.e611-618.e617.  https://doi.org/10.1016/j.ajog.2018.03.013 CrossRefGoogle Scholar
  6. 6.
    Budorick NE, Figueroa R, Vizcarra M, Shin J (2017) Another look at ultrasound and magnetic resonance imaging for diagnosis of placenta accreta. J Matern Fetal Neonatal Med:1-6.  https://doi.org/10.1080/14767058.2016.1252744
  7. 7.
    D’Antonio F, Iacovella C, Palacios-Jaraquemada J, Bruno CH, Manzoli L, Bhide A (2014) Prenatal identification of invasive placentation using magnetic resonance imaging: systematic review and meta-analysis. Ultrasound Obstet Gynecol 44 (1):8-16.  https://doi.org/10.1002/uog.13327 CrossRefGoogle Scholar
  8. 8.
    Matsubara S, Takahashi H, Takei Y (2018) Magnetic resonance imaging for diagnosis of placenta accreta spectrum disorders: still useful for real-world practice. Am J Obstet Gynecol 219 (3):312-313.  https://doi.org/10.1016/j.ajog.2018.04.058 CrossRefGoogle Scholar
  9. 9.
    Mar WA, Berggruen S, Atueyi U, Sekhon S, Garzon SA, Knuttinen MG, McGahan JP (2015) Ultrasound imaging of placenta accreta with MR correlation. Ultrasound Q 31 (1):23-33.  https://doi.org/10.1097/ruq.0000000000000127 CrossRefGoogle Scholar
  10. 10.
    Jauniaux E, Chantraine F, Silver RM, Langhoff-Roos J (2018) FIGO consensus guidelines on placenta accreta spectrum disorders: Epidemiology. Int J Gynaecol Obstet 140 (3):265-273.  https://doi.org/10.1002/ijgo.12407 CrossRefGoogle Scholar
  11. 11.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 37 (5):1483-1503.  https://doi.org/10.1148/rg.2017170056 CrossRefGoogle Scholar
  12. 12.
    Modzelewski R, Janvresse E, de la Rue T, Vera P (2012) Comparison of heterogeneity quantification algorithms for brain SPECT perfusion images. EJNMMI Res 2 (1):40.  https://doi.org/10.1186/2191-219x-2-40 CrossRefGoogle Scholar
  13. 13.
    Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A (2016) Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol 85 (4):824-829.  https://doi.org/10.1016/j.ejrad.2016.01.013 CrossRefGoogle Scholar
  14. 14.
    Herlidou-Même S, Constans JM, Carsin B, Olivie D, Eliat PA, Nadal-Desbarats L, Gondry C, Le Rumeur E, Idy-Peretti I, de Certaines JD (2003) MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magn Reson Imaging 21 (9):989-993CrossRefGoogle Scholar
  15. 15.
    Kassner A, Thornhill RE (2010) Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol 31 (5):809-816.  https://doi.org/10.3174/ajnr.a2061 CrossRefGoogle Scholar
  16. 16.
    Zhang Y (2012) MRI texture analysis in multiple sclerosis. Int J Biomed Imaging 2012:762804.  https://doi.org/10.1155/2012/762804 Google Scholar
  17. 17.
    Kotu LP, Engan K, Skretting K, Måløy F, Orn S, Woie L, Eftestøl T (2013) Probability mapping of scarred myocardium using texture and intensity features in CMR images. Biomed Eng Online 12:91.  https://doi.org/10.1186/1475-925x-12-91 CrossRefGoogle Scholar
  18. 18.
    Herlidou S, Rolland Y, Bansard JY, Le Rumeur E, de Certaines JD (1999) Comparison of automated and visual texture analysis in MRI: characterization of normal and diseased skeletal muscle. Magn Reson Imaging 17 (9):1393-1397CrossRefGoogle Scholar
  19. 19.
    Alpert K, Kogan A, Parrish T, Marcus D, Wang L (2016) The Northwestern University Neuroimaging Data Archive (NUNDA). Neuroimage 124 (Pt B):1131-1136.  https://doi.org/10.1016/j.neuroimage.2015.05.060 CrossRefGoogle Scholar
  20. 20.
    Tiwari P, Prasanna P, Rogers L, Wolansky L, Badve C, Sloan A, Cohen M, Madabhushi A (2014) Texture Descriptors to distinguish Radiation Necrosis from Recurrent Brain Tumors on multi-parametric MRI. Proc SPIE 9035:90352B.  https://doi.org/10.1117/12.2043969 Google Scholar
  21. 21.
    Derman AY, Nikac V, Haberman S, Zelenko N, Opsha O, Flyer M (2011) MRI of placenta accreta: a new imaging perspective. AJR American journal of roentgenology 197 (6):1514-1521.  https://doi.org/10.2214/AJR.10.5443 CrossRefGoogle Scholar
  22. 22.
    Teo TH, Law YM, Tay KH, Tan BS, Cheah FK (2009) Use of magnetic resonance imaging in evaluation of placental invasion. Clin Radiol 64 (5):511-516.  https://doi.org/10.1016/j.crad.2009.02.003 CrossRefGoogle Scholar
  23. 23.
    Lax A, Prince MR, Mennitt KW, Schwebach JR, Budorick NE (2007) The value of specific MRI features in the evaluation of suspected placental invasion. Magn Reson Imaging 25 (1):87-93.  https://doi.org/10.1016/j.mri.2006.10.007 CrossRefGoogle Scholar
  24. 24.
    Baughman WC, Corteville JE, Shah RR (2008) Placenta accreta: spectrum of US and MR imaging findings. Radiographics 28 (7):1905-1916.  https://doi.org/10.1148/rg.287085060 CrossRefGoogle Scholar
  25. 25.
    Lim PS, Greenberg M, Edelson MI, Bell KA, Edmonds PR, Mackey AM (2011) Utility of ultrasound and MRI in prenatal diagnosis of placenta accreta: a pilot study. AJR American journal of roentgenology 197 (6):1506-1513.  https://doi.org/10.2214/AJR.11.6858 CrossRefGoogle Scholar
  26. 26.
    Kim JH, Ko ES, Lim Y, Lee KS, Han BK, Ko EY, Hahn SY, Nam SJ (2017) Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes. Radiology 282 (3):665-675.  https://doi.org/10.1148/radiol.2016160261 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of MedicineUniversity of Illinois at ChicagoChicagoUSA
  2. 2.Department of RadiologyUniversity of Illinois at ChicagoChicagoUSA
  3. 3.Northwestern Memorial HospitalChicagoUSA
  4. 4.Department of RadiologyUniversity of California, San FranciscoSan FranciscoUSA

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