Voxel level radiologic-pathologic validation of Restriction Spectrum Imaging cellularity index with Gleason grade in Prostate Cancer
Natalie M Schenker-Ahmed1, Ghiam Yamin1, Ahmed Shabaik1, Dennis Adams1, Hauke Bartsch1, Joshua Kuperman1, Nathan S White1, Rebecca A Rakow-Penner1, Kevin McCammack1, J Kellogg Parsons1, Christopher Kane1, Anders Dale1, and David Karow1

1UC-San Diego, La Jolla, CA, United States

Synopsis

Current multiparametric magnetic resonance imaging techniques for detecting prostate cancer are limited with respect to tumor conspicuity assessment, in vivo characterization and localization. We demonstrate that a novel diffusion-based MRI technique, restriction spectrum imaging (RSI-MRI), differentiates among benign, low-grade and high-grade PCa at voxel-level resolution. Using an RSI-MRI index to differentiate between low- and high-grade categories of tumor PCa aggressiveness may help improve and refine diagnosis and staging of PCa. Additionally, because it can detect intratumor variation, RSI-MRI may have particular relevance for planning targeted therapies such as radiation seed therapy placement, MR-guided focused ultrasound surgery, and MR-guided targeted biopsy.

Introduction

Multiparametric magnetic resonance imaging is a rapidly evolving non-invasive diagnostic tool that has been used to complement other emerging biomarkers in the screening, staging, monitoring, and treatment of prostate cancer (PCa)1. However, prostate MRI is confounded by variable sensitivity and specificity, which curtails its clinical utility2,3. Restriction spectrum imaging (RSI-MRI) is an advanced diffusion imaging technique that shows improved conspicuity and differentiation of solid tumors compared to traditional diffusion weighted imaging4,5. RSI-MRI can differentiate hindered from restricted diffusion, thought to correspond to the extracellular and intracellular water compartments, respectively6. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive PCa as measured by Gleason grade (GG)7, and that RSI-MRI improves sensitivity to the detection of extraprostatic extension of prostate cancer8. Here we evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated PCa regions.

Experimental Design

Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified and demarcated by an uropathologist. The whole mount H&E stained prostate sections were scanned at high resolution (75μm/pixel). The scanned images were reconstructed into a “digital prostate map” interface and overlaid with a grid of tiles corresponding to voxel dimensions. Each grid tile was graded using the GG system. An experienced radiologist selected the slice from the presurgical T2 MR series that most closely corresponded to the plane of the histopathology section. Deformation of the histology section was corrected for by transforming the T2 and corresponding RSI-MRI slice to the size and shape of the histopathology section. The RSI-MRI cellularity index was calculated from the RSI-MRI data and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity.

Results

Using a linear mixed-effect model with a random effect of subject9, RSI-MRI cellularity index was found to distinguish between PCa and benign tumor (t=25.48, p<0.00001). Significant differences were also found between benign tissue and PCa classified as low-grade (GG=3; t=11.56, p<0.001) or high-grade (GG≥4 t=24.03, p<0.001). Furthermore, RSI-MRI differentiated between low and high-grade PCa (t=3.23, p=0.003).

Conclusions

Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. The relationship between GG and RSI-MRI means that RSI-MRI can be used as a component of active surveillance to non-invasively detect high-grade cancer and affect staging and treatment. Furthermore, because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may have particular relevance for planning of focal procedures, such as MRI guided targeted biopsies and targeted radiotherapy, where identifying the area with the most aggressive disease is particularly important.

Acknowledgements

This work was supported by the Department of Defense (DoD) Grant, Prostate Cancer Research Program (#W81XWH-13-1-0391), the American Cancer Society—Institutional Research Grant (#70-002), UCSD Clinician Scientist Program (#5T32EB005970-07), UCSD School of Medicine Microscopy Core, and NINDS P30 core grant (#NS047101), and General Electric, Investigator Initiated Research Award BOK92325. This material is based upon work supported by the National Science Foundation under Grant No. 1430082.

References

1. Johnson LM, Turkbey B, Figg WD, Choyke PL. Multiparametric MRI in prostate cancer management. Nat Rev Clin Oncol 2014;11:346–53.

2. Wu LM, Xu JR, Ye YQ, Lu Q, Hu JN. The clinical value of diffusion-weighted imaging in combination with T2-weighted imaging in diagnosing prostate carcinoma: A systematic review and meta-analysis. Am J Roentgenol 2012;199:103–10.

3. Isebaert S, Van Den Bergh L, Haustermans K, Joniau S, Lerut E, De Wever L, et al. Multiparametric MRI for prostate cancer localization in correlation to whole-mount histopathology. J Magn Reson Imaging 2013;37:1392–401.

4. White NS, Leergaard TB, D’Arceuil H, Bjaalie JG, Dale AM. Probing tissue microstructure with restriction spectrum imaging: Histological and theoretical validation. Hum Brain Mapp 2013;34:327–46.

5. Farid N, Almeida-Freitas DB, White NS, McDonald CR, Kuperman JM, Almutairi A a., et al. Combining diffusion and perfusion differentiates tumor from bevacizumab-related imaging abnormality (bria). J Neurooncol 2014;120:539–46.

6. White NS, McDonald CR, Farid N, Kuperman J, Karow D, Schenker-Ahmed NM, et al. Diffusion-Weighted Imaging in Cancer: Physical Foundations and Applications of Restriction Spectrum Imaging. Cancer Res 2014;74:4638–52.

7. Liss MA, White NS, Parsons JK, Schenker-Ahmed NM, Rakow-Penner R, Kuperman JM, et al. MRI-Derived Restriction Spectrum Imaging Cellularity Index is Associated with High Grade Prostate Cancer on Radical Prostatectomy Specimens. Front Oncol 2015;5:1–8.

8. Rakow-Penner RA, White NS, Parsons JK, Choi HW, Liss M a, Kuperman JM, et al. Novel technique for characterizing prostate cancer utilizing MRI restriction spectrum imaging: proof of principle and initial clinical experience with extraprostatic extension. Prostate Cancer Prostatic Dis 2015;18:81–5.

9. Bates D, Maechler M, Bolker B, Walker S. lme4: Linear mixed-effects models using Eigen and S4. R Packag. version 1.1-7, 2014.

Figures

“Digital prostate map” of a histopathological section and example grid overlay. (A) H&E-stained WM histopathological prostate; Scale bar=1cm. (B) “Digital prostate map” constructed to overlay the section, corresponding to the outlined box in (A) surrounding the tumor ROI. (C-F) Representative tiles showing different Gleason grades at 10X magnification. Scale bar=75.5μm.

A) T2-weighted MR images and B) RSI-MRI z-score maps, after in-plane transformation to register with histopathology, blue=boundary of prostate from histopathology; Grid overlay color-coded for C) RSI-MRI z-score, D) Gleason grade, E) H&E-stained histopathological section with tumor outlined, F) Mean RSI-MRI z-score vs Gleason grade at voxel-level resolution. Error bars=SEM.

Mean RSI-MRI cellularity index represented as a z-score corresponding to histological Gleason grade using data from all voxels graded in all cases. Error bars represent the standard errors of the mean.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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