Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Towards Population-Based Histologic Stain Normalization of Glioblastoma

  • Conference paper
  • First Online:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (BrainLes 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11992))

Included in the following conference series:

Abstract

Glioblastoma (‘GBM’) is the most aggressive type of primary malignant adult brain tumor, with very heterogeneous radiographic, histologic, and molecular profiles. A growing body of advanced computational analyses are conducted towards further understanding the biology and variation in glioblastoma. To address the intrinsic heterogeneity among different computational studies, reference standards have been established to facilitate both radiographic and molecular analyses, e.g., anatomical atlas for image registration and housekeeping genes, respectively. However, there is an apparent lack of reference standards in the domain of digital pathology, where each independent study uses an arbitrarily chosen slide from their evaluation dataset for normalization purposes. In this study, we introduce a novel stain normalization approach based on a composite reference slide comprised of information from a large population of anatomically annotated hematoxylin and eosin (‘H&E’) whole-slide images from the Ivy Glioblastoma Atlas Project (‘IvyGAP’). Two board-certified neuropathologists manually reviewed and selected annotations in 509 slides, according to the World Health Organization definitions. We computed summary statistics from each of these approved annotations and weighted them based on their percent contribution to overall slide (‘PCOS’), to form a global histogram and stain vectors. Quantitative evaluation of pre- and post-normalization stain density statistics for each annotated region with PCOS\(\,>\,0.05\)% yielded a significant (largest p\(\,=\,\)0.001, two-sided Wilcoxon rank sum test) reduction of its intensity variation for both ‘H’ & ‘E’. Subject to further large-scale evaluation, our findings support the proposed approach as a potentially robust population-based reference for stain normalization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ostrom, Q., et al.: Females have the survival advantage in glioblastoma. Neuro-Oncology 20, 576–577 (2018)

    Google Scholar 

  2. Herrlinger, U., et al.: Lomustine-temozolomide combination therapy versus standard temozolomide therapy in patients with newly diagnosed glioblastoma with methylated MGMT promoter (CeTeG/NOA-09): a randomised, open-label, phase 3 trial. Lancet 393, 678–688 (2019)

    Google Scholar 

  3. Sottoriva, A., et al.: Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl. Acad. Sci. 110, 4009–4014 (2013)

    Google Scholar 

  4. Clark, K., et al.: The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013). https://doi.org/10.1007/s10278-013-9622-7

    Article  Google Scholar 

  5. Scarpace, L., et al.: Radiology data from the cancer genome atlas glioblastoma [TCGA-GBM] collection. Cancer Imaging Arch. 11(4) (2016)

    Google Scholar 

  6. Verhaak, R.G.W., et al.: Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17(1), 98–110 (2010)

    Google Scholar 

  7. Cancer Genome Atlas Research Network: Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N. Engl. J. Med. 372(26), 2481–2498 (2015)

    Google Scholar 

  8. Beers, A., et al.: DICOM-SEG conversions for TCGA-LGG and TCGA-GBM segmentation datasets. Cancer Imaging Arch. (2018)

    Google Scholar 

  9. Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017)

    Google Scholar 

  10. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Cancer Imaging Arch. 286 (2017)

    Google Scholar 

  11. Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Cancer Imaging Arch. (2017)

    Google Scholar 

  12. Gevaert, O.: Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273(1), 168–174 (2014)

    Google Scholar 

  13. Gutman, D.A., et al.: MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267(2), 560–569 (2013)

    Google Scholar 

  14. Binder, Z., et al.: Epidermal growth factor receptor extracellular domain mutations in glioblastoma present opportunities for clinical imaging and therapeutic development. Cancer Cell 34, 163–177 (2018)

    Google Scholar 

  15. Jain, R.: Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 272(2), 484–93 (2014)

    Google Scholar 

  16. Aerts, H.J.W.L.: The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12), 1636–1642 (2016)

    Google Scholar 

  17. Davatzikos, C., et al.: Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome. J. Med. Imaging 5(1), 011018 (2018)

    Google Scholar 

  18. Bilello, M., et al.: Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma. NeuroImage: Clin. 12, 34–40 (2016)

    Google Scholar 

  19. Akbari, H., et al.: In vivo evaluation of EGFRvIII mutation in primary glioblastoma patients via complex multiparametric MRI signature. Neuro-Oncology 20(8), 1068–1079 (2018)

    Google Scholar 

  20. Bakas, S., et al.: In vivo detection of EGFRvIII in glioblastoma via perfusion magnetic resonance imaging signature consistent with deep peritumoral infiltration: the \(\varphi \)-index. Clin. Cancer Res. 23, 4724–4734 (2017)

    Google Scholar 

  21. Zwanenburg, A., et al.: Image biomarker standardisation initiative, arXiv:1612.07003 (2016)

  22. Lambin, P., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441–446 (2012)

    Google Scholar 

  23. Haralick, R.M., et al.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Google Scholar 

  24. Galloway, M.M.: Texture analysis using grey level run lengths. Comput. Graph. Image Process. 4, 172–179 (1975)

    Google Scholar 

  25. Chu, A., et al.: Use of gray value distribution of run lengths for texture analysis. Pattern Recogn. Lett. 11, 415–419 (1990)

    MATH  Google Scholar 

  26. Dasarathy, B.V., Holder, E.B.: Image characterizations based on joint gray level—run length distributions. Pattern Recogn. Lett. 12, 497–502 (1991)

    Google Scholar 

  27. Tang, X.: Texture information in run-length matrices. IEEE Trans. Image Process. 7, 1602–1609 (1998)

    Google Scholar 

  28. Amadasun, M., King, R.: Textural features corresponding to textural properties. IEEE Trans. Syst. Man Cybern. 19, 1264–1274 (1989)

    Google Scholar 

  29. Jain, A.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  30. Macenko, M., et al.: A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1107–1110 (2009)

    Google Scholar 

  31. Reinhard, E., et al.: Color transfer between images. IEEE Comput. Graphics Appl. 21(5), 34–41 (2001)

    Google Scholar 

  32. Vahadane, A., et al.: Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962–1971 (2016)

    Google Scholar 

  33. Khan, A., et al.: A non-linear mapping approach to stain normalisation in digital histopathology images using image-specific colour deconvolution. IEEE Trans. Biomed. Eng. 61(6), 1729–1738 (2014)

    Google Scholar 

  34. Shaban, M.T., et al.: StainGAN: stain style transfer for digital histological images. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI), pp. 953–956 (2019)

    Google Scholar 

  35. Janowczyk, A., et al.: Stain normalization using sparse AutoEncoders (StaNoSA). Comput. Med. Imaging Graph. 50–61, 2017 (2017)

    Google Scholar 

  36. Bianconi, F., Kather, J.N., Reyes-Aldasoro, C.C.: Evaluation of colour pre-processing on patch-based classification of H&E-stained images. In: Reyes-Aldasoro, C.C., Janowczyk, A., Veta, M., Bankhead, P., Sirinukunwattana, K. (eds.) ECDP 2019. LNCS, vol. 11435, pp. 56–64. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23937-4_7

    Chapter  Google Scholar 

  37. Puchalski, R., et al.: An anatomic transcriptional atlas of human glioblastoma. Science 360, 660–663 (2018)

    Google Scholar 

  38. Shah, N., et al.: Data from Ivy GAP. Cancer Imaging Arch. (2016)

    Google Scholar 

  39. Ruifrok, A., Johnston, D.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291–299 (2001)

    Google Scholar 

  40. Mairal, J., et al.: Online learning for matrix factorization and sparse coding. J. Mach. Learn. Res. 11, 19–60 (2010)

    MathSciNet  MATH  Google Scholar 

  41. Rabinovich, A., et al.: Unsupervised color decomposition of histologically stained tissue samples. In: Advances in Neural Information Processing Systems, vol. 16, pp. 667–674 (2004)

    Google Scholar 

  42. Li, X., et al.: A complete color normalization approach to histopathology images using color cues computed from saturation-weighted statistics. IEEE Trans. Biomed. Eng. 62(7), 1862–1873 (2015)

    Google Scholar 

Download references

Acknowledgement

Research reported in this publication was partly supported by the National Institutes of Health (NIH) under award numbers NIH/NINDS:R01NS042645, NIH/NCI:U24CA189523, NIH/NCATS:UL1TR001878, and by the Institute for Translational Medicine and Therapeutics (ITMAT) of the University of Pennsylvania. The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH, or the ITMAT of the UPenn.

Author information

Authors and Affiliations

  1. Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA

    Caleb M. Grenko, Hamed Akbari & Spyridon Bakas

  2. Center for Interdisciplinary Studies, Davidson College, Davidson, NC, USA

    Caleb M. Grenko

  3. Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA

    Angela N. Viaene

  4. Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    MacLean P. Nasrallah, Michael D. Feldman & Spyridon Bakas

  5. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hamed Akbari & Spyridon Bakas

Authors
  1. Caleb M. Grenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Angela N. Viaene
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. MacLean P. Nasrallah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael D. Feldman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hamed Akbari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Spyridon Bakas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Spyridon Bakas .

Editor information

Editors and Affiliations

  1. University Hospital of Zurich, Zurich, Switzerland

    Alessandro Crimi

  2. University of Pennsylvania, Philadelphia, PA, USA

    Spyridon Bakas

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Grenko, C.M., Viaene, A.N., Nasrallah, M.P., Feldman, M.D., Akbari, H., Bakas, S. (2020). Towards Population-Based Histologic Stain Normalization of Glioblastoma. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2019. Lecture Notes in Computer Science(), vol 11992. Springer, Cham. https://doi.org/10.1007/978-3-030-46640-4_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-46640-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-46639-8

  • Online ISBN: 978-3-030-46640-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation