Dynamische kontrastmittelverstärkte MRT (DCE-MRI) zur automatischen Erkennung von residualen oder rezidivierenden Krebsherden nach Prostatektomie

Translated title of the contribution: Dynamic contrast-enhanced MRI for automatic detection of foci of residual or recurrent disease after prostatectomy

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Purpose: This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. Materials and methods: Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the “tumor” pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0, …, DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. Results: Principal component analysis determined 2–4 significant patterns in patients’ DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). Ktrans for DCE2.5 was statistically higher than the GTV’s Ktrans (p < 0.05), indicating that the automatic volume better captures areas of malignancy. Conclusion: A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system.

Original languageGerman
Pages (from-to)1-9
Number of pages9
JournalStrahlentherapie und Onkologie
DOIs
StateAccepted/In press - Oct 19 2016

Fingerprint

Prostatectomy
Tumor Burden
Prostate
Prostate-Specific Antigen
Radiotherapy
Software
Weights and Measures
Principal Component Analysis
Neoplasms
Prostatic Neoplasms
Pharmacokinetics
Therapeutics

Keywords

  • Dynamic contrast enhanced (DCE-)MRI
  • Pattern recognition, automated
  • Prostate bed
  • Prostate cancer
  • Salvage radiotherapy

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging

Cite this

@article{12de4beb721147acacac25a0025f5418,
title = "Dynamische kontrastmittelverst{\"a}rkte MRT (DCE-MRI) zur automatischen Erkennung von residualen oder rezidivierenden Krebsherden nach Prostatektomie",
abstract = "Purpose: This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. Materials and methods: Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the “tumor” pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0, …, DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. Results: Principal component analysis determined 2–4 significant patterns in patients’ DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). Ktrans for DCE2.5 was statistically higher than the GTV’s Ktrans (p < 0.05), indicating that the automatic volume better captures areas of malignancy. Conclusion: A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system.",
keywords = "Dynamic contrast enhanced (DCE-)MRI, Pattern recognition, automated, Prostate bed, Prostate cancer, Salvage radiotherapy",
author = "Parra, {N. Andres} and Amber Orman and Kyle Padgett and Victor Casillas and Sanoj Punnen and Abramowitz, {Matthew C} and Alan Pollack and Radka Stoyanova",
year = "2016",
month = "10",
day = "19",
doi = "10.1007/s00066-016-1055-z",
language = "German",
pages = "1--9",
journal = "Strahlentherapie und Onkologie",
issn = "0179-7158",
publisher = "Urban und Vogel",

}

TY - JOUR

T1 - Dynamische kontrastmittelverstärkte MRT (DCE-MRI) zur automatischen Erkennung von residualen oder rezidivierenden Krebsherden nach Prostatektomie

AU - Parra, N. Andres

AU - Orman, Amber

AU - Padgett, Kyle

AU - Casillas, Victor

AU - Punnen, Sanoj

AU - Abramowitz, Matthew C

AU - Pollack, Alan

AU - Stoyanova, Radka

PY - 2016/10/19

Y1 - 2016/10/19

N2 - Purpose: This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. Materials and methods: Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the “tumor” pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0, …, DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. Results: Principal component analysis determined 2–4 significant patterns in patients’ DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). Ktrans for DCE2.5 was statistically higher than the GTV’s Ktrans (p < 0.05), indicating that the automatic volume better captures areas of malignancy. Conclusion: A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system.

AB - Purpose: This study aimed to develop an automated procedure for identifying suspicious foci of residual/recurrent disease in the prostate bed using dynamic contrast-enhanced-MRI (DCE-MRI) in prostate cancer patients after prostatectomy. Materials and methods: Data of 22 patients presenting for salvage radiotherapy (RT) with an identified gross tumor volume (GTV) in the prostate bed were analyzed retrospectively. An unsupervised pattern recognition method was used to analyze DCE-MRI curves from the prostate bed. Data were represented as a product of a number of signal-vs.-time patterns and their weights. The temporal pattern, characterized by fast wash-in and gradual wash-out, was considered the “tumor” pattern. The corresponding weights were thresholded based on the number (1, 1.5, 2, 2.5) of standard deviations away from the mean, denoted as DCE1.0, …, DCE2.5, and displayed on the T2-weighted MRI. The resultant four volumes were compared with the GTV and maximum pre-RT prostate-specific antigen (PSA) level. Pharmacokinetic modeling was also carried out. Results: Principal component analysis determined 2–4 significant patterns in patients’ DCE-MRI. Analysis and display of the identified suspicious foci was performed in commercial software (MIM Corporation, Cleveland, OH, USA). In general, DCE1.0/DCE1.5 highlighted larger areas than GTV. DCE2.0 and GTV were significantly correlated (r = 0.60, p < 0.05). DCE2.0/DCA2.5 were also significantly correlated with PSA (r = 0.52, 0.67, p < 0.05). Ktrans for DCE2.5 was statistically higher than the GTV’s Ktrans (p < 0.05), indicating that the automatic volume better captures areas of malignancy. Conclusion: A software tool was developed for identification and visualization of the suspicious foci in DCE-MRI from post-prostatectomy patients and was integrated into the treatment planning system.

KW - Dynamic contrast enhanced (DCE-)MRI

KW - Pattern recognition, automated

KW - Prostate bed

KW - Prostate cancer

KW - Salvage radiotherapy

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