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Title: SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy

Abstract

Purpose: To automatically identify and outline suspicious regions of recurrent or residual disease in the prostate bed using Dynamic Contrast Enhanced-MRI (DCE-MRI) in patients after prostatectomy. Methods: Twenty-two patients presenting for salvage radiotherapy and with identified Gross Tumor Volume (GTV) in the prostate bed were retrospectively analyzed. The MRI data consisted of Axial T2weighted-MRI (T2w) of the pelvis: resolution 1.25×1.25×2.5 mm; Field of View (FOV): 320×320 mm; slice thickness=2.5mm; 72 slices; and Dynamic Contrast Enhanced MRI (DCE-MRI)–12 series of T1w with identical spatial resolution to T2w and at 30–34s temporal resolution. Unsupervised pattern recognition was used to decompose the 4D DCE data as the product W.H of weights W of k patterns H. A well-perfused pattern Hwp was identified and the weight map Wwp associated to Hwp was used to delineate suspicious volumes. Threshold of Wwp set at mean(Wwp)+S*std(Wwp), S=1,1.5,2 and 2.5 defined four volumes labeled as DCE1.0 to DCE2.5. These volumes were displayed on T2w and, along with GTV, were correlated with the highest pre-treatment PSA values, and with pharmacokinetic analysis constants. Results: GTV was significantly correlated with DCE2.0(ρ= 0.60, p<0.003), and DCE 2.5 (ρ=0.58, p=0.004)). Significant correlation was found between highest pre-treatment PSA and GTV(ρ=0.42, p<0.049), DCE2.0(ρ= 0.52,more » p<0.012), and DCE 2.5 (ρ=0.67, p<<0.01)). Kruskal-Wallis analysis showed that Ktrans median value was statistically different between non-specific prostate bed tissue NSPBT and both GTV (p<<0.001) and DCE2.5 (p<<0.001), but while median Ve was statistically different between DCE2.5 and NSPBT (p=0.002), it was not statistically different between GTV and NSPBT (p=0.054), suggesting that automatic volumes capture more accurately the area of malignancy. Conclusion: Software developed for identification and visualization of suspicions regions in DCE-MRI from post-prostatectomy patients has been validated by PSA and pharmacokinetic constants analysis showing that it generates clinically relevant volumes.« less

Authors:
; ; ;  [1]
  1. University of Miami, Miami, FL (United States)
Publication Date:
OSTI Identifier:
22626748
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANIMAL TISSUES; COMPUTER CODES; NEOPLASMS; NMR IMAGING; PATIENTS; PATTERN RECOGNITION; PELVIS; PROSTATE; RADIOTHERAPY; SPATIAL RESOLUTION; THICKNESS

Citation Formats

Parra, N A, Abramowitz, M, Pollack, A, and Stoyanova, R. SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy. United States: N. p., 2016. Web. doi:10.1118/1.4955797.
Parra, N A, Abramowitz, M, Pollack, A, & Stoyanova, R. SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy. United States. doi:10.1118/1.4955797.
Parra, N A, Abramowitz, M, Pollack, A, and Stoyanova, R. Wed . "SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy". United States. doi:10.1118/1.4955797.
@article{osti_22626748,
title = {SU-F-R-25: Automatic Identification of Suspicious Recurrent/residual Disease Regions After Prostatectomy},
author = {Parra, N A and Abramowitz, M and Pollack, A and Stoyanova, R},
abstractNote = {Purpose: To automatically identify and outline suspicious regions of recurrent or residual disease in the prostate bed using Dynamic Contrast Enhanced-MRI (DCE-MRI) in patients after prostatectomy. Methods: Twenty-two patients presenting for salvage radiotherapy and with identified Gross Tumor Volume (GTV) in the prostate bed were retrospectively analyzed. The MRI data consisted of Axial T2weighted-MRI (T2w) of the pelvis: resolution 1.25×1.25×2.5 mm; Field of View (FOV): 320×320 mm; slice thickness=2.5mm; 72 slices; and Dynamic Contrast Enhanced MRI (DCE-MRI)–12 series of T1w with identical spatial resolution to T2w and at 30–34s temporal resolution. Unsupervised pattern recognition was used to decompose the 4D DCE data as the product W.H of weights W of k patterns H. A well-perfused pattern Hwp was identified and the weight map Wwp associated to Hwp was used to delineate suspicious volumes. Threshold of Wwp set at mean(Wwp)+S*std(Wwp), S=1,1.5,2 and 2.5 defined four volumes labeled as DCE1.0 to DCE2.5. These volumes were displayed on T2w and, along with GTV, were correlated with the highest pre-treatment PSA values, and with pharmacokinetic analysis constants. Results: GTV was significantly correlated with DCE2.0(ρ= 0.60, p<0.003), and DCE 2.5 (ρ=0.58, p=0.004)). Significant correlation was found between highest pre-treatment PSA and GTV(ρ=0.42, p<0.049), DCE2.0(ρ= 0.52, p<0.012), and DCE 2.5 (ρ=0.67, p<<0.01)). Kruskal-Wallis analysis showed that Ktrans median value was statistically different between non-specific prostate bed tissue NSPBT and both GTV (p<<0.001) and DCE2.5 (p<<0.001), but while median Ve was statistically different between DCE2.5 and NSPBT (p=0.002), it was not statistically different between GTV and NSPBT (p=0.054), suggesting that automatic volumes capture more accurately the area of malignancy. Conclusion: Software developed for identification and visualization of suspicions regions in DCE-MRI from post-prostatectomy patients has been validated by PSA and pharmacokinetic constants analysis showing that it generates clinically relevant volumes.},
doi = {10.1118/1.4955797},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}