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Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate

  • Pallavi Tiwari
  • Satish Viswanath
  • John Kurhanewicz
  • Anant Madabhushi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6963)

Abstract

Recently, multi-parametric (MP) Magnetic Resonance (MR) Imaging (T2-weighted, MR Spectroscopy (MRS), Diffusion-weighted (DWI)) has shown great potential for evaluating the early effects of radiotherapy (RT) in the prostate. In this work we present a framework for quantitatively combining MP-MRI markers in order to assess RT changes on a voxel-by-voxel basis. The suite of segmentation, registration, feature extraction, and classifier tools presented in this work will allow for identification of (a) residual disease, and (b) new foci of cancer (local recurrence) within the prostate. Our scheme involves, (a) simultaneously evaluating differences in pre-, post-RT MR imaging markers, and (b) intelligently integrating and weighting the imaging marker changes obtained in (a) to generate a combined MP-MRI difference map that can better quantify treatment specific changes in the prostate. We demonstrate the applicability of our scheme in studying intensity-modulated radiation therapy (IMRT)-related changes for a cohort of 14 MP (T2w, MRS, DWI) prostate MRI patient datasets. In the first step, the different MRI protocols from pre- and post-IMRT MRI scans are affinely registered (accounting for gland shrinkage), followed by automated segmentation of the prostate capsule using an active shape model. Individual imaging marker difference maps are generated by calculating the differences of textural, metabolic, and functional MRI marker attributes, pre- and post-RT, on a per-voxel basis. These difference maps are then combined via an intelligent optimization scheme to generate a combined weighted difference map, where higher difference values on the map signify larger change (new foci of cancer), and low difference values signify no/small change post-RT. In the absence of histological ground truth (surgical or biopsy), radiologist delineated CaP extent on pre-, and post-RT MRI was employed as the ground truth for evaluating the accuracy of our scheme in successfully identifying MP-MRI related disease changes post-RT. A mean area under the receiver operating curve (AUC) of 73.2% was obtained via the weighted MP-MRI map, when evaluated against expert delineated CaP extent on pre-, post-RT MRI. The difference maps corresponding to the individual structural (T2w intensities), functional (ADC intensities) and metabolic (choline, creatine) markers yielded a corresponding mean AUC of 54.4%, 68.6% and 70.8%.

Keywords

Imaging Marker Active Shape Model Receiver Operating Curve Curve Prostate Capsule Elastic Registration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Pucar, D., et al.: The role of imaging in the detection of prostate cancer local recurrence after radiation therapy and surgery. Curr. Opin. Urol. 18(1), 87–97 (2008)CrossRefGoogle Scholar
  2. 2.
    Chen, M., et al.: Prostate cancer detection: Comparison of T2-w Imaging, DWI, proton MRSI, and the three techniques combined. Acta Radiologica 49(5), 602–610 (2008)CrossRefGoogle Scholar
  3. 3.
    Kurhanewicz, J., et al.: Locally recurrent prostate cancer after EBRT: Diagnostic performance of 1.5T endorectal MRI and MRSI for detection. Radiology 256(2), 485–492 (2010)CrossRefGoogle Scholar
  4. 4.
    Kim, C.K., et al.: Prediction of locally recurrent prostate cancer after radiation therapy: Incremental value of 3T diffusion-weighted MRI. JMRI 29(2), 391–397 (2009)CrossRefGoogle Scholar
  5. 5.
    Coakley, F.V., et al.: Endorectal MRI and MRSI for locally recurrent prostate cancer after external beam radiation therapy: preliminary experience. Radiology 233(2), 441–448 (2004)CrossRefGoogle Scholar
  6. 6.
    Song, I., et al.: Assessment of Response to Radiotherapy for Prostate Cancer: Value of Diffusion-Weighted MRI at 3T. Am. J. Roentgenol. 194(6), 477–482 (2010)CrossRefGoogle Scholar
  7. 7.
    Westphalen, A., et al.: T2-Weighted endorectal MR imaging of prostate cancer after external beam radiation therapy. Int. Braz. J. Urol. 35(2), 171–180 (2009)CrossRefGoogle Scholar
  8. 8.
    Zapotoczna, A., et al.: Current role and future perspectives of MRS in radiation oncology for prostate cancer. Neoplasia 9(6), 455–463 (2007)CrossRefGoogle Scholar
  9. 9.
    Tiwari, P., et al.: Multimodal wavelet embedding representation for data combination: Integrating MRI and MRS for prostate cancer detection. NMR in Biomed (accepted 2011)Google Scholar
  10. 10.
    Langer, D., et al.: Prostate cancer detection with multi-parametric MRI: Logistic regression analysis of quantitative T2, DWI, and DCE MRI. JMRI 30(2), 327–334 (2009)CrossRefGoogle Scholar
  11. 11.
    Ozer, S., et al.: Supervised and unsupervised methods for prostate cancer segmentation with multispectral mri. Med. Phy. 37, 1873–1883 (2010)CrossRefGoogle Scholar
  12. 12.
    Chappelow, J., et al.: Elastic Registration of Multimodal Prostate MRI and Histology via Multi-Attribute Combined Mutual Information. Med. Phys. 38(4), 2005–2018 (2010)CrossRefGoogle Scholar
  13. 13.
    Toth, R., et al.: A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation. Med. Img. Anal. 15, 214–225 (2010)CrossRefGoogle Scholar
  14. 14.
    Madabhushi, A., Feldman, M., Metaxas, D., Tomaszewski, J., Chute, D.: Automated detection of prostatic adenocarcinoma from high resolution ex vivo mri. IEEE Transactions on Medical Imaging 24, 1611–1625 (2005)CrossRefGoogle Scholar
  15. 15.
    Mazaheri, Y., et al.: Prostate cancer: Identification with combined diffusion-weighted MRI and 3d 1H MRSI correlation with pathologic findings. Radiology 246(2), 480–488 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Pallavi Tiwari
    • 1
  • Satish Viswanath
    • 1
  • John Kurhanewicz
    • 2
  • Anant Madabhushi
    • 1
  1. 1.Department of Biomedical EngineeringRutgers UniversityUSA
  2. 2.Department of RadiologyUniversity of CaliforniaSan FranciscoUSA

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