Correlation of magnetic resonance imaging with digital histopathology in prostate

  • Jin Tae Kwak
  • Sandeep Sankineni
  • Sheng Xu
  • Baris Turkbey
  • Peter L. Choyke
  • Peter A. Pinto
  • Maria Merino
  • Bradford J. WoodEmail author
Original Article



We propose a systematic approach to correlate MRI and digital histopathology in prostate.


T2-weighted (T2W) MRI and diffusion-weighted imaging (DWI) are acquired, and a patient-specific mold (PSM) is designed from the MRI. Following prostatectomy, a whole mount tissue specimen is placed in the PSM and sectioned, ensuring that tissue blocks roughly correspond to MRI slices. Rigid body and thin plate spline deformable registration attempt to correct deformation during image acquisition and tissue preparation and achieve a more complete one-to-one correspondence between MRIs and tissue sections. Each tissue section is stained with hematoxylin and eosin and segmented by adopting a machine learning approach. Utilizing this tissue segmentation and image registration, the density of cellular and tissue components (lumen, nucleus, epithelium, and stroma) is estimated per MR voxel, generating density maps for the whole prostate.


This study was approved by the local IRB, and informed consent was obtained from all patients. Registration of tissue specimens and MRIs was aided by the PSM and subsequent image registration. Tissue segmentation was performed using a machine learning approach, achieving \(\ge \)0.98 AUCs for lumen, nucleus, epithelium, and stroma. Examining the density map of tissue components, significant differences were observed between cancer, benign peripheral zone, and benign prostatic hyperplasia (p value \(<\)5e\(-\)2). Similarly, the signal intensity of the corresponding areas in both T2W MRI and DWI was significantly different (p value \(<\)1e\(-\)10).


The proposed approach is able to correlate MRI and digital histopathology of the prostate and is promising as a potential tool to facilitate a more cellular and zonal tissue-based analysis of prostate MRI, based upon a correlative histopathology perspective.


Prostate Histopathology Image registration Machine learning 


Compliance with ethical standards

Conflict of interest

Peter L. Choyke, Peter A. Pinto, and Bradford J. Wood have a cooperative research and development agreement with Philips Healthcare. Jin Tae Kwak, Sandeep Sankineni, Baris Turkbey, Sheng Xu, and Maria Merino declare that they have no conflict of interest.


  1. 1.
    Siegel R, Ma JM, Zou ZH, Jemal A (2014) Cancer statistics, 2014. CA Cancer J Clin 64(1):9–29CrossRefPubMedGoogle Scholar
  2. 2.
    Glaessgen A, Hamberg H, Pihl CG, Sundelin B, Nilsson B, Egevad L (2004) Interobserver reproducibility of modified Gleason score in radical prostatectomy specimens. Virchows Arch 445(1):17–21. doi: 10.1007/s00428-004-1034-0 PubMedGoogle Scholar
  3. 3.
    Allsbrook WC, Mangold KA, Johnson MH, Lane RB, Lane CG, Amin MB, Bostwick DG, Humphrey PA, Jones EC, Reuter VE, Sakr W, Sesterhenn IA, Troncoso P, Wheeler TM, Epstein JI (2001) Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists. Hum Pathol 32(1):74–80. doi: 10.1053/hupa.2001.21134 CrossRefPubMedGoogle Scholar
  4. 4.
    Turkbey B, Mani H, Shah V, Rastinehad AR, Bernardo M, Pohida T, Pang YX, Daar D, Benjamin C, McKinney YL, Trivedi H, Chua C, Bratslavsky G, Shih JH, Linehan WM, Merino MJ, Choyke PL, Pinto PA (2011) Multiparametric 3T prostate magnetic resonance imaging to detect cancer: histopathological correlation using prostatectomy specimens processed in customized magnetic resonance imaging based molds. J Urol 186(5):1818–1824CrossRefPubMedGoogle Scholar
  5. 5.
    Habchi H, Bratan F, Paye A, Pagnoux G, Sanzalone T, Mege-Lechevallier F, Crouzet S, Colombel M, Rabilloud M, Rouviere O (2014) Value of prostate multiparametric magnetic resonance imaging for predicting biopsy results in first or repeat biopsy. Clin Radiol 69(3):e120–128CrossRefPubMedGoogle Scholar
  6. 6.
    Kwak JT, Hewitt SM, Sinha S, Bhargava R (2011) Multimodal microscopy for automated histologic analysis of prostate cancer. BMC Cancer 11:62CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, West RB, van de Rijn M, Koller D (2011) Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 3(108):108ra113PubMedGoogle Scholar
  8. 8.
    Doyle S, Feldman MD, Shih N, Tomaszewski J, Madabhushi A (2012) Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer. BMC Bioinform 13:282CrossRefGoogle Scholar
  9. 9.
    Quint L, Van Erp J, Bland P, Del Buono E, Mandell SH, Grossman H, Gikas P (1991) Prostate cancer: correlation of MR images with tissue optical density at pathologic examination. Radiology 179(3):837–842CrossRefPubMedGoogle Scholar
  10. 10.
    Schiebler ML, Tomaszewski JE, Bezzi M, Pollack HM, Kressel HY, Cohen EK, Altman HG, Gefter WB, Wein AJ, Axel L (1989) Prostatic carcinoma and benign prostatic hyperplasia: correlation of high-resolution MR and histopathologic findings. Radiology 172(1):131–137CrossRefPubMedGoogle Scholar
  11. 11.
    Wang XZ, Wang B, Gao ZQ, Liu JG, Liu ZQ, Niu QL, Sun ZK, Yuan YX (2009) Diffusion-weighted imaging of prostate cancer: correlation between apparent diffusion coefficient values and tumor proliferation. J Magn Reson Imaging 29(6):1360–1366CrossRefPubMedGoogle Scholar
  12. 12.
    Zelhof B, Pickles M, Liney G, Gibbs P, Rodrigues G, Kraus S, Turnbull L (2009) Correlation of diffusion-weighted magnetic resonance data with cellularity in prostate cancer. BJU Int 103(7):883–888CrossRefPubMedGoogle Scholar
  13. 13.
    Gibbs P, Liney GP, Pickles MD, Zelhof B, Rodrigues G, Turnbull LW (2009) Correlation of ADC and T2 measurements with cell density in prostate cancer at 3.0 Tesla. Investig Radiol 44(9):572–576CrossRefGoogle Scholar
  14. 14.
    Langer DL, van der Kwast TH, Evans AJ, Plotkin A, Trachtenberg J, Wilson BC, Haider MA (2010) Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K trans, ve, and corresponding histologic features 1. Radiology 255(2):485–494CrossRefPubMedGoogle Scholar
  15. 15.
    Turkbey B, Shah VP, Pang Y, Bernardo M, Xu S, Kruecker J, Locklin J, Baccala AA Jr, Rastinehad AR, Merino MJ (2011) Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? Radiology 258(2):488–495CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Liu P, Wang SJ, Turkbey B, Grant K, Pinto P, Choyke P, Wood BJ, Summers RM (2013) A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels. In: SPIE medical imaging, 2013. International Society for Optics and Photonics, pp 86701G-86701G-86706Google Scholar
  17. 17.
    Shah V, Pohida T, Turkbey B, Mani H, Merino M, Pinto PA, Choyke P, Bernardo M (2009) A method for correlating in vivo prostate magnetic resonance imaging and histopathology using individualized magnetic resonance-based molds. Rev Sci Instrum 80(10):14301. doi: 10.1063/1.3242697
  18. 18.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal 24(7):971–987CrossRefGoogle Scholar
  19. 19.
    Guo ZH, Li Q, You J, Zhang D, Liu WH (2012) Local directional derivative pattern for rotation invariant texture classification. Neural Comput Appl 21(8):1893–1904Google Scholar
  20. 20.
    Koço S, Capponi C (2011) A boosting approach to multiview classification with cooperation. In: Machine learning and knowledge discovery in databases. Springer, Berlin, pp 209–228Google Scholar
  21. 21.
    Vapnik VN (1995) The nature of statistical learning theory. Springer, New YorkCrossRefGoogle Scholar
  22. 22.
    Bookstein FL (1989) Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans Pattern Anal 11(6):567–585. doi: 10.1109/34.24792 CrossRefGoogle Scholar
  23. 23.
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12(1):77CrossRefGoogle Scholar
  24. 24.
    Kalavagunta C, Zhou X, Schmechel SC, Metzger GJ (2014) Registration of in vivo prostate MRI and pseudo-whole mount histology using local affine transformations guided by internal structures (LATIS). J Magn Reson Imaging. doi: 10.1002/jmri.24629

Copyright information

© CARS 2015

Authors and Affiliations

  • Jin Tae Kwak
    • 1
  • Sandeep Sankineni
    • 2
  • Sheng Xu
    • 1
  • Baris Turkbey
    • 2
  • Peter L. Choyke
    • 2
  • Peter A. Pinto
    • 3
  • Maria Merino
    • 4
  • Bradford J. Wood
    • 1
    Email author
  1. 1.Center for Interventional OncologyNational Institutes of HealthBethesdaUSA
  2. 2.Molecular Imaging Program, National Cancer InstituteNational Institutes of HealthBethesdaUSA
  3. 3.Urologic Oncology Branch, National Cancer InstituteNational Institutes of HealthBethesdaUSA
  4. 4.Laboratory of Pathology, National Cancer InstituteNational Institutes of HealthBethesdaUSA

Personalised recommendations