Next Article in Journal
Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme
Previous Article in Journal
Developing a Pipeline for Multiparametric MRI-Guided Radiation Therapy: Initial Results from a Phase II Clinical Trial in Newly Diagnosed Glioblastoma
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tomography
Volume 5
Issue 1
10.18383/j.tom.2018.00033
tomography-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space

by
Sean D. McGarry
1,
John D. Bukowy
1,
Kenneth A. Iczkowski
2,
Jackson G. Unteriner
1,
Petar Duvnjak
1,
Allison K. Lowman
1,
Kenneth Jacobsohn
3,
Mark Hohenwalter
1,
Michael O. Griffin
1,
Alex W. Barrington
1,
Halle E. Foss
1,
Tucker Keuter
4,
Sarah L. Hurrell
1,
William A. See
3,
Marja T. Nevalainen
5,6,
Anjishnu Banerjee
4 and
Peter S. LaViolette
1,7,*
1
Departments of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
2
Departments of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
3
Departments of Urological Surgery, Medical College of Wisconsin, Milwaukee, WI, USA
4
Departments of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
5
Departments of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, USA
6
Departments of Pharmacology, Medical College of Wisconsin, Milwaukee, WI, USA
7
Departments of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 127-134; https://doi.org/10.18383/j.tom.2018.00033
Submission received: 5 December 2018 / Revised: 2 January 2019 / Accepted: 5 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

Prostate cancer is the most common noncutaneous cancer in men in the United States. The current paradigm for screening and diagnosis is imperfect, with relatively low specificity, high cost, and high morbidity. This study aims to generate new image contrasts by learning a distribution of unique image signatures associated with prostate cancer. In total, 48 patients were prospectively recruited for this institutional review board–approved study. Patients underwent multiparametric magnetic resonance imaging 2 weeks before surgery. Postsurgical tissues were annotated by a pathologist and aligned to the in vivo imaging. Radiomic profiles were generated by linearly combining 4 image contrasts (T2, apparent diffusion coefficient [ADC] 0-1000, ADC 50-2000, and dynamic contrast-enhanced) segmented using global thresholds. The distribution of radiomic profiles in high-grade cancer, low-grade cancer, and normal tissues was recorded, and the generated probability values were applied to a naive test set. The resulting Gleason probability maps were stable regardless of training cohort, functioned independent of prostate zone, and outperformed conventional clinical imaging (area under the curve [AUC] = 0.79). Extensive overlap was seen in the most common image signatures associated with high- and low-grade cancer, indicating that low- and high-grade tumors present similarly on conventional imaging.
Keywords: prostate Cancer; radiomics; rad-path; radio-pathomics prostate Cancer; radiomics; rad-path; radio-pathomics

Share and Cite

MDPI and ACS Style

McGarry, S.D.; Bukowy, J.D.; Iczkowski, K.A.; Unteriner, J.G.; Duvnjak, P.; Lowman, A.K.; Jacobsohn, K.; Hohenwalter, M.; Griffin, M.O.; Barrington, A.W.; et al. Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space. Tomography 2019, 5, 127-134. https://doi.org/10.18383/j.tom.2018.00033

AMA Style

McGarry SD, Bukowy JD, Iczkowski KA, Unteriner JG, Duvnjak P, Lowman AK, Jacobsohn K, Hohenwalter M, Griffin MO, Barrington AW, et al. Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space. Tomography. 2019; 5(1):127-134. https://doi.org/10.18383/j.tom.2018.00033

Chicago/Turabian Style

McGarry, Sean D., John D. Bukowy, Kenneth A. Iczkowski, Jackson G. Unteriner, Petar Duvnjak, Allison K. Lowman, Kenneth Jacobsohn, Mark Hohenwalter, Michael O. Griffin, Alex W. Barrington, and et al. 2019. "Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space" Tomography 5, no. 1: 127-134. https://doi.org/10.18383/j.tom.2018.00033

APA Style

McGarry, S. D., Bukowy, J. D., Iczkowski, K. A., Unteriner, J. G., Duvnjak, P., Lowman, A. K., Jacobsohn, K., Hohenwalter, M., Griffin, M. O., Barrington, A. W., Foss, H. E., Keuter, T., Hurrell, S. L., See, W. A., Nevalainen, M. T., Banerjee, A., & LaViolette, P. S. (2019). Gleason Probability Maps: A Radiomics Tool for Mapping Prostate Cancer Likelihood in MRI Space. Tomography, 5(1), 127-134. https://doi.org/10.18383/j.tom.2018.00033

Article Metrics

Back to TopTop