Next Article in Journal
Phantom Validation of DCE-MRI Magnitude and Phase-Based Vascular Input Function Measurements
Previous Article in Journal
Real-Time Quantitative Assessment of Accuracy and Precision of Blood Volume Derived from DCE-MRI in Individual Patients during a Clinical Trial
 
 
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.2018.00037
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

Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers

by
Nestor Andres Parra
1,
Hong Lu
1,2,
Jung Choi
3,
Kenneth Gage
3,
Julio Pow-Sang
4,
Robert J. Gillies
1,3 and
Yoganand Balagurunathan
1,*
1
Department of Cancer Physiology, H.L. Moffitt Cancer Center, Tampa, FL 33612, USA; [email protected]
2
Department of Radiology, Tianjin Medical University Cancer, Institute and Hospital, Tianjin, China
3
Department of Radiology, and H.L. Moffitt Cancer Center, Tampa, FL 33612, USA
4
Department of Urology, H.L. Moffitt Cancer Center, Tampa, FL 33612, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 68-76; https://doi.org/10.18383/j.tom.2018.00037
Submission received: 15 December 2018 / Revised: 10 January 2019 / Accepted: 11 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

Prostate cancer identification and assessment of clinical significance continues to be a challenge. Routine multiparametric magnetic resonance imaging has shown to be useful in assessing disease progression. Although dynamic contrast-enhanced imaging (DCE) has the ability to characterize perfusion across time and has shown enormous utility, radiological assessment (Prostate Imaging-Reporting and Data System or PIRADS version 2) has limited its use owing to lack of consistency and nonquantitative nature. In our work, we propose a systematic methodology to quantify perfusion dynamics for the DCE imaging. Using these metrics, 7 different subregions or perfusion habitats of the targeted lesions are localized and related to clinical significance. We found that quantitative features describing the habitat based on the late area under the DCE time-activity curve was a good predictor of clinical significance disease. The best predictive feature in the habitat had an AUC of 0.82, CI [0.81–0.83].
Keywords: MRI; prostate cancer; machine learning; radiomics; habitats; DCE MRI; prostate cancer; machine learning; radiomics; habitats; DCE

Share and Cite

MDPI and ACS Style

Parra, N.A.; Lu, H.; Choi, J.; Gage, K.; Pow-Sang, J.; Gillies, R.J.; Balagurunathan, Y. Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers. Tomography 2019, 5, 68-76. https://doi.org/10.18383/j.tom.2018.00037

AMA Style

Parra NA, Lu H, Choi J, Gage K, Pow-Sang J, Gillies RJ, Balagurunathan Y. Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers. Tomography. 2019; 5(1):68-76. https://doi.org/10.18383/j.tom.2018.00037

Chicago/Turabian Style

Parra, Nestor Andres, Hong Lu, Jung Choi, Kenneth Gage, Julio Pow-Sang, Robert J. Gillies, and Yoganand Balagurunathan. 2019. "Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers" Tomography 5, no. 1: 68-76. https://doi.org/10.18383/j.tom.2018.00037

APA Style

Parra, N. A., Lu, H., Choi, J., Gage, K., Pow-Sang, J., Gillies, R. J., & Balagurunathan, Y. (2019). Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers. Tomography, 5(1), 68-76. https://doi.org/10.18383/j.tom.2018.00037

Article Metrics

Back to TopTop