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European Radiology

, Volume 27, Issue 10, pp 4209–4217 | Cite as

Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study

  • Mirabela RusuEmail author
  • Prabhakar Rajiah
  • Robert Gilkeson
  • Michael Yang
  • Christopher Donatelli
  • Rajat Thawani
  • Frank J. Jacono
  • Philip Linden
  • Anant MadabhushiEmail author
Computer Applications

Abstract

Objective

To develop an approach for radiology-pathology fusion of ex vivo histology of surgically excised pulmonary nodules with pre-operative CT, to radiologically map spatial extent of the invasive adenocarcinomatous component of the nodule.

Methods

Six subjects (age: 75 ± 11 years) with pre-operative CT and surgically excised ground-glass nodules (size: 22.5 ± 5.1 mm) with a significant invasive adenocarcinomatous component (>5 mm) were included. The pathologist outlined disease extent on digitized histology specimens; two radiologists and a pulmonary critical care physician delineated the entire nodule on CT (in-plane resolution: <0.8 mm, inter-slice distance: 1–5 mm). We introduced a novel reconstruction approach to localize histology slices in 3D relative to each other while using CT scan as spatial constraint. This enabled the spatial mapping of the extent of tumour invasion from histology onto CT.

Results

Good overlap of the 3D reconstructed histology and the nodule outlined on CT was observed (65.9 ± 5.2%). Reduction in 3D misalignment of corresponding anatomical landmarks on histology and CT was observed (1.97 ± 0.42 mm). Moreover, the CT attenuation (HU) distributions were different when comparing invasive and in situ regions.

Conclusion

This proof-of-concept study suggests that our fusion method can enable the spatial mapping of the invasive adenocarcinomatous component from 2D histology slices onto in vivo CT.

Key Points

3D reconstructions are generated from 2D histology specimens of ground glass nodules.

The reconstruction methodology used pre-operative in vivo CT as 3D spatial constraint.

The methodology maps adenocarcinoma extent from digitized histology onto in vivo CT.

The methodology potentially facilitates the discovery of CT signature of invasive adenocarcinoma.

Keywords

Lung adenocarcinoma Computed tomography Pathology Computer-assisted image processing Multimodal imaging 

Abbreviations

2D

Two dimensional

3D

Three dimensional

HU

Hounsfield units

mm

millimetre

ml

Millilitres

msec

Millisecond

mAs

Miliampere second

mSV

Milisieverts

Notes

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Mirabela Rusu.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: GE Global Research, Elucid Bioimaging and Inspirata Inc.

Funding

This study has received funding from the US Department of Defense (W81XWH-13-1-0487), US National Institutes of Health (R01CA202752-01A1, R01CA208236-01A1, R21CA179327-01, R21CA19515201, U24CA199374-01), the US National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098503-02), the US Department of Defense Prostate Cancer Synergistic Idea Development Award (PC120857); the US Department of Defense Lung Cancer Idea Development Award (LC130463); the US Department of Defense Prostate Cancer Idea Development Award; US Department of Defense Peer Reviewed Medical Research Program (W81XWH-16-1-0329); the Case Comprehensive Cancer Center Pilot Grant, the VelaSano Grant from the Cleveland Clinic, Ohio, US the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University, Ohio, US. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Statistics and biometry

One of the authors has significant statistical expertise.

Ethical approval

Institutional Review Board approval was obtained.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Methodology

• retrospective

• experimental

• performed at one institution

Supplementary material

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References

  1. 1.
    Yanagawa N, Shiono S, Abiko M, Ogata S-y, Sato T, Tamura G (2013) New IASLC/ATS/ERS classification and invasive tumor size are predictive of disease recurrence in stage I lung adenocarcinoma. J Thorac Oncol 8:612–618CrossRefPubMedGoogle Scholar
  2. 2.
    Godoy MCB, Naidich DP (2009) Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: recommended interim guidelines for assessment and management. Radiology 253:606–622CrossRefPubMedGoogle Scholar
  3. 3.
    Van Schil PE, Sihoe ADL, Travis WD (2013) Pathologic classification of adenocarcinoma of lung. J Surg Oncol 108:320–326CrossRefPubMedGoogle Scholar
  4. 4.
    Borczuk AC, Qian F, Kazeros A (2009) Invasive size is an independent predictor of survival in pulmonary adenocarcinoma. Am J Surg Pathol 33:462–469CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Xiang W, Xing Y, Jiang S et al (2014) Morphological factors differentiating between early lung adenocarcinomas appearing as pure ground-glass nodules measuring 10 mm on thin-section computed tomography. Cancer Imaging 14:33CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Lee SM, Park CM, Goo JM, Lee H-J, Wi JY, Kang CH (2013) Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiology 268:265–273CrossRefPubMedGoogle Scholar
  7. 7.
    Chae H-D, Park CM, Park SJ, Lee SM, Kim KG, Goo JM (2014) Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 273:285–293CrossRefPubMedGoogle Scholar
  8. 8.
    Orooji M, Rusu M, Rajiah P, Yang M, Jacono F, Gilkeson RC et al (2014) computer extracted texture features on CT predict level of invasion in ground glass non-small cell lung nodules. In: Radiology society of north america, annual meeting proceedingsGoogle Scholar
  9. 9.
    Son JY, Lee HY, Lee KS et al (2014) Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS One 9, e104066CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Chappelow J, Bloch BN, Rofsky N et al (2011) Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information. Med Phys 38:2005–18CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ward AD, Crukley C, McKenzie CA et al (2012) Prostate: Registration of Digital Histopathologic Images to in Vivo MR Images Acquired by Using Endorectal Receive Coil. Radiology 263:856–864CrossRefPubMedGoogle Scholar
  12. 12.
    Rusu M, Golden T, Wang H, Gow A, Madabhushi A (2015) Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model. Med Phys 42:4822–4832CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Onozato ML, Klepeis VE, Yagi Y, Mino-Kenudson M (2012) A role of three-dimensional (3D)-reconstruction in the classification of lung adenocarcinoma. Anal Cell Pathol 35:79–84CrossRefGoogle Scholar
  14. 14.
    Litzlbauer HD, Neuhaeuser C, Moell A et al (2006) Three-dimensional imaging and morphometric analysis of alveolar tissue from microfocal X-ray-computed tomography. Am J Physiol Lung Cell Mol Physiol 291:L535–L545CrossRefPubMedGoogle Scholar
  15. 15.
    Pieper S, Halle M, Kikinis R (2004) 3D Slicer. IEEE Int Sym Biomed Imaging :632–635Google Scholar
  16. 16.
    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  17. 17.
    Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary. Proc Am Thorac Soc 8:381–385CrossRefPubMedGoogle Scholar
  18. 18.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2010) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Pattern Anal Mach Intell 29:196–205Google Scholar
  19. 19.
    Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1:80–83CrossRefGoogle Scholar
  20. 20.
    Lotz J, Berger J, Müller B, Breuhahn K, Grabe N, Heldmann S et al (2014) Zooming in: high resolution 3D reconstruction of differently stained histological whole slide images. In: SPIE Medical Imaging, p 904104–1–904104–7Google Scholar

Copyright information

© European Society of Radiology 2017

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringCase Western Reserve UniversityClevelandUSA
  2. 2.GE Global ResearchNiskayunaUSA
  3. 3.UT Southwestern Medical CenterDallasUSA
  4. 4.University HospitalsCleveland Medical Center and Case Western Reserve UniversityClevelandUSA
  5. 5.Louis Stokes Cleveland VA Medical CenterClevelandUSA

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