Literature DB >> 24850842

Prostate MRI: evaluating tumor volume and apparent diffusion coefficient as surrogate biomarkers for predicting tumor Gleason score.

Olivio F Donati1, Asim Afaq2, Hebert Alberto Vargas3, Yousef Mazaheri4, Junting Zheng5, Chaya S Moskowitz5, Hedvig Hricak3, Oguz Akin6.   

Abstract

PURPOSE: To investigate whether tumor volume derived from apparent diffusion coefficient (ADC) maps (VolumeADC) and tumor mean ADC value (ADCmean) are independent predictors of prostate tumor Gleason score (GS). EXPERIMENTAL
DESIGN: Tumor volume and GS were recorded from whole-mount histopathology for 131 men (median age, 60 years) who underwent endorectal diffusion-weighted MRI for local staging of prostate cancer before prostatectomy. VolumeADC and ADCmean were derived from ADC maps and correlated with histopathologic tumor volume and GS. Univariate and multivariate analyses were performed to evaluate prediction of tumor aggressiveness. Areas under receiver-operating characteristics curves (AUC) were calculated to evaluate the performance of VolumeADC and ADCmean in discriminating tumors of GS 6 and GS ≥7.
RESULTS: Histopathology identified 116 tumor foci >0.5 mL. VolumeADC correlated significantly with histopathologic tumor volume (ρ = 0.683). The correlation increased with increasing GS (ρ = 0.453 for GS 6 tumors; ρ = 0.643 for GS 7 tumors; ρ = 0.980 for GS ≥8 tumors). Both VolumeADC (ρ = 0.286) and ADCmean (ρ = -0.309) correlated with GS. At univariate analysis, both VolumeADC (P = 0.0325) and ADCmean (P = 0.0033) could differentiate GS = 6 from GS ≥7 tumor foci. However, at multivariate analysis, only ADCmean (P = 0.0156) was a significant predictor of tumor aggressiveness (i.e., GS 6 vs. GS ≥7). For differentiating GS 6 from GS ≥7 tumors, AUCs were 0.644 and 0.704 for VolumeADC and ADCmean, respectively, and 0.749 for both parameters combined.
CONCLUSION: In patients with prostate cancer, ADCmean is an independent predictor of tumor aggressiveness, but VolumeADC is not. The latter parameter adds little to the ADCmean in predicting tumor GS. ©2014 American Association for Cancer Research.

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Year:  2014        PMID: 24850842     DOI: 10.1158/1078-0432.CCR-14-0044

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  38 in total

1.  Assessment of Prostate Cancer Aggressiveness by Use of the Combination of Quantitative DWI and Dynamic Contrast-Enhanced MRI.

Authors:  Andreas M Hötker; Yousef Mazaheri; Ömer Aras; Junting Zheng; Chaya S Moskowitz; Tatsuo Gondo; Kazuhiro Matsumoto; Hedvig Hricak; Oguz Akin
Journal:  AJR Am J Roentgenol       Date:  2016-02-22       Impact factor: 3.959

2.  Detection of Clinically Significant Prostate Cancer: Short Dual-Pulse Sequence versus Standard Multiparametric MR Imaging-A Multireader Study.

Authors:  Borna K Barth; Pieter J L De Visschere; Alexander Cornelius; Carlos Nicolau; Hebert Alberto Vargas; Daniel Eberli; Olivio F Donati
Journal:  Radiology       Date:  2017-03-27       Impact factor: 11.105

3.  Prostate Cancer: assessing the effects of androgen-deprivation therapy using quantitative diffusion-weighted and dynamic contrast-enhanced MRI.

Authors:  Andreas M Hötker; Yousef Mazaheri; Junting Zheng; Chaya S Moskowitz; Joshua Berkowitz; Joshua E Lantos; Xin Pei; Michael J Zelefsky; Hedvig Hricak; Oguz Akin
Journal:  Eur Radiol       Date:  2015-03-29       Impact factor: 5.315

4.  Quantitative study of prostate cancer using three dimensional fiber tractography.

Authors:  Sandeep Hedgire; Alexey Tonyushkin; Aoife Kilcoyne; Jason A Efstathiou; Peter F Hahn; Mukesh Harisinghani
Journal:  World J Radiol       Date:  2016-04-28

5.  Prostate cancer bone metastases on staging prostate MRI: prevalence and clinical features associated with their diagnosis.

Authors:  Hebert Alberto Vargas; Rachel Schor-Bardach; Niamh Long; Anna N Kirzner; Jane D Cunningham; Debra A Goldman; Chaya S Moskowitz; Ramon E Sosa; Evis Sala; David M Panicek; Hedvig Hricak
Journal:  Abdom Radiol (NY)       Date:  2017-01

6.  Diffusion-weighted imaging of the prostate: should we use quantitative metrics to better characterize focal lesions originating in the peripheral zone?

Authors:  Thibaut Pierre; Francois Cornud; Loïc Colléter; Frédéric Beuvon; Frantz Foissac; Nicolas B Delongchamps; Paul Legmann
Journal:  Eur Radiol       Date:  2017-11-22       Impact factor: 5.315

Review 7.  Interactive Feature Space Explorer© for multi-modal magnetic resonance imaging.

Authors:  Alpay Özcan; Barış Türkbey; Peter L Choyke; Oguz Akin; Ömer Aras; Seong K Mun
Journal:  Magn Reson Imaging       Date:  2015-04-11       Impact factor: 2.546

8.  Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.

Authors:  Duc Fehr; Harini Veeraraghavan; Andreas Wibmer; Tatsuo Gondo; Kazuhiro Matsumoto; Herbert Alberto Vargas; Evis Sala; Hedvig Hricak; Joseph O Deasy
Journal:  Proc Natl Acad Sci U S A       Date:  2015-11-02       Impact factor: 11.205

9.  Comparative Effectiveness of Targeted Prostate Biopsy Using Magnetic Resonance Imaging Ultrasound Fusion Software and Visual Targeting: a Prospective Study.

Authors:  Daniel J Lee; Pedro Recabal; Daniel D Sjoberg; Alan Thong; Justin K Lee; James A Eastham; Peter T Scardino; Hebert Alberto Vargas; Jonathan Coleman; Behfar Ehdaie
Journal:  J Urol       Date:  2016-03-30       Impact factor: 7.450

10.  Prognostic Value of Pretreatment MRI in Patients With Prostate Cancer Treated With Radiation Therapy: A Systematic Review and Meta-Analysis.

Authors:  Sungmin Woo; Sangwon Han; Tae-Hyung Kim; Chong Hyun Suh; Antonio C Westphalen; Hedvig Hricak; Michael J Zelefsky; Hebert Alberto Vargas
Journal:  AJR Am J Roentgenol       Date:  2019-12-04       Impact factor: 3.959

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