Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness

Purpose To develop a robust and clinically applicable automated method for analyzing Dynamic Contrast Enhanced (DCE-) MRI of the prostate as a guide for targeted biopsies and treatments. Materials and methods An unsupervised pattern recognition (PR) method was used to analyze prostate DCE-MRI from 71 sequential radiotherapy patients. Identified regions of interest (ROIs) with increased perfusion were assigned either to the peripheral (PZ) or transition zone (TZ). Six quantitative features, associated with the washin and washout part of the weighted average DCE curve from the ROI, were calculated. The associations between the assigned DCE-scores and Gleason Score (GS) were investigated. A heatmap of tumor aggressiveness covering the entire prostate was generated and validated with histopathology from MRI-ultrasound fused (MRI-US) targeted biopsies. Results The volumes of the PR-identified ROI’s were significantly correlated with the highest GS from the biopsy session for each patient. Following normalization (and only after normalization) with gluteus maximus muscle’s DCE signal, the quantitative features in PZ were significantly correlated with GS. These correlations straightened in subset of patients with available MRI-US biopsies when GS from the individual biopsies were used. Area under the receiver operating characteristics curve for discrimination between indolent vs aggressive cancer for the significant quantitative features reached 0.88–0.95. When DCE-scores were calculated in normal appearing tissues, the features were highly discriminative for cancer vs no cancer both in PZ and TZ. The generated heatmap of tumor aggressiveness coincided with the location and GS of the MRI-US biopsies. Conclusion A quantitative approach for DCE-MRI analysis was developed. The resultant map of aggressiveness correlated well with tumor location and GS and is applicable for integration in radiotherapy/radiology imaging software for clinical translation.

[1]  Lawrence Barnthouse,et al.  Issues and Recommendations , 2007 .

[2]  Lars Egevad,et al.  Contemporary Grading for Prostate Cancer: Implications for Patient Care ☆ , 2013 .

[3]  D. Kuban,et al.  Long-term failure patterns and survival in a randomized dose-escalation trial for prostate cancer. Who dies of disease? , 2008, International journal of radiation oncology, biology, physics.

[4]  Nico Karssemeijer,et al.  Computer-Aided Detection of Prostate Cancer in MRI , 2014, IEEE Transactions on Medical Imaging.

[5]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[6]  Walter H Backes,et al.  The precision of pharmacokinetic parameters in dynamic contrast-enhanced magnetic resonance imaging: the effect of sampling frequency and duration , 2011, Physics in medicine and biology.

[7]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[8]  Jason A Koutcher,et al.  Mapping Tumor Hypoxia In Vivo Using Pattern Recognition of Dynamic Contrast-enhanced MRI Data. , 2012, Translational oncology.

[9]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[10]  W. J. Morris,et al.  ASCENDE-RT: An Analysis of Health-Related Quality of Life for a Randomized Trial Comparing Low-Dose-Rate Brachytherapy Boost With Dose-Escalated External Beam Boost for High- and Intermediate-Risk Prostate Cancer. , 2017, International Journal of Radiation Oncology, Biology, Physics.

[11]  Karin Haustermans,et al.  Single blind randomized Phase III trial to investigate the benefit of a focal lesion ablative microboost in prostate cancer (FLAME-trial): study protocol for a randomized controlled trial , 2011, Trials.

[12]  R. Mclean,et al.  A Unified Approach to Mixed Linear Models , 1991 .

[13]  Radka Stoyanova,et al.  Denoising of MR spectroscopic imaging data using statistical selection of principal components , 2016, Magnetic Resonance Materials in Physics, Biology and Medicine.

[14]  W. J. Morris,et al.  ASCENDE-RT: An Analysis of Treatment-Related Morbidity for a Randomized Trial Comparing a Low-Dose-Rate Brachytherapy Boost with a Dose-Escalated External Beam Boost for High- and Intermediate-Risk Prostate Cancer. , 2017, International journal of radiation oncology, biology, physics.

[15]  K. Wallner,et al.  Long-Term Outcomes for Patients with Prostate Cancer Having Intermediate and High-Risk Disease, Treated with Combination External Beam Irradiation and Brachytherapy , 2010, Journal of oncology.

[16]  H. Huisman,et al.  Accurate estimation of pharmacokinetic contrast‐enhanced dynamic MRI parameters of the prostate , 2001, Journal of magnetic resonance imaging : JMRI.

[17]  Tristan Barrett,et al.  Dynamic contrast-enhanced MRI of prostate cancer at 3 T: a study of pharmacokinetic parameters. , 2007, AJR. American journal of roentgenology.

[18]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[19]  C. Schraml,et al.  Optimized Fast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of the Prostate: Effect of Sampling Duration on Pharmacokinetic Parameters , 2016, Investigative radiology.

[20]  Olivier Rouvière,et al.  Evaluation of T2-weighted and dynamic contrast-enhanced MRI in localizing prostate cancer before repeat biopsy , 2009, European Radiology.

[21]  J. Mao,et al.  Local infiltration with cocktail analgesics during 2 level lumbar spinal fusion surgery , 2019, Medicine.

[22]  R. Stoyanova,et al.  Dynamic contrast-enhanced MRI for automatic detection of foci of residual or recurrent disease after prostatectomy , 2016, Strahlentherapie und Onkologie.

[23]  Thomas E. Yankeelov,et al.  The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge , 2016, Tomography.

[24]  L. Schour,et al.  Excellent Results From High Dose Rate Brachytherapy and External Beam for Prostate Cancer are Not Improved by Androgen Deprivation , 2009, American journal of clinical oncology.

[25]  J R Griffiths,et al.  Clinical studies. , 2005, Advances in pharmacology.

[26]  Ron Kikinis,et al.  Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. , 2014, Translational oncology.

[27]  Thomas Hambrock,et al.  Assessment of prostate cancer aggressiveness using dynamic contrast-enhanced magnetic resonance imaging at 3 T. , 2013, European urology.

[28]  A. Villers,et al.  Dynamic contrast enhanced, pelvic phased array magnetic resonance imaging of localized prostate cancer for predicting tumor volume: correlation with radical prostatectomy findings. , 2006, The Journal of urology.

[29]  Karin Haustermans,et al.  Evaluation of semi-quantitative dynamic contrast-enhanced MRI parameters for prostate cancer in correlation to whole-mount histopathology. , 2012, European journal of radiology.

[30]  P. Bruni,et al.  An Active Form of Sphingosine Kinase-1 Is Released in the Extracellular Medium as Component of Membrane Vesicles Shed by Two Human Tumor Cell Lines , 2010, Journal of oncology.

[31]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[32]  A. Nelson,et al.  TU-CD-BRA-04: Evaluation of An Atlas-Based Segmentation Method for Prostate and Peripheral Zone Regions On MRI , 2015 .

[33]  A. Jemal,et al.  Cancer statistics, 2015 , 2015, CA: a cancer journal for clinicians.

[34]  Baris Turkbey,et al.  Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI , 2017, Scientific Reports.

[35]  Karin Haustermans,et al.  Multiparametric MRI for prostate cancer localization in correlation to whole‐mount histopathology , 2013, Journal of magnetic resonance imaging : JMRI.

[36]  C. Ménard,et al.  Boosting imaging defined dominant prostatic tumors: a systematic review. , 2013, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[37]  M. Knopp,et al.  Estimating kinetic parameters from dynamic contrast‐enhanced t1‐weighted MRI of a diffusable tracer: Standardized quantities and symbols , 1999, Journal of magnetic resonance imaging : JMRI.

[38]  W. I. Tseng,et al.  Washout gradient in dynamic contrast‐enhanced MRI is associated with tumor aggressiveness of prostate cancer , 2012, Journal of magnetic resonance imaging : JMRI.