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A simulation study comparing nine mathematical models of arterial input function for dynamic contrast enhanced MRI to the Parker model. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:507-518. [DOI: 10.1007/s13246-018-0632-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2017] [Accepted: 03/20/2018] [Indexed: 02/06/2023]
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52
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Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers. Sci Data 2018; 5:180008. [PMID: 29437167 PMCID: PMC5810424 DOI: 10.1038/sdata.2018.8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Accepted: 12/18/2017] [Indexed: 02/05/2023] Open
Abstract
Dynamic myraidpro contrast-enhanced magnetic resonance imaging (DCE-MRI) has been correlated with prognosis in head and neck squamous cell carcinoma as well as with changes in normal tissues. These studies implement different software, either commercial or in-house, and different scan protocols. Thus, the generalizability of the results is not confirmed. To assist in the standardization of quantitative metrics to confirm the generalizability of these previous studies, this data descriptor delineates in detail the DCE-MRI digital imaging and communications in medicine (DICOM) files with DICOM radiation therapy (RT) structure sets and digital reference objects (DROs), as well as, relevant clinical data that encompass a data set that can be used by all software for comparing quantitative metrics. Variable flip angle (VFA) with six flip angles and DCE-MRI scans with a temporal resolution of 5.5 s were acquired in the axial direction on a 3T MR scanner with a field of view of 25.6 cm, slice thickness of 4 mm, and 256×256 matrix size.
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Kargar S, Borisch EA, Froemming AT, Kawashima A, Mynderse LA, Stinson EG, Trzasko JD, Riederer SJ. Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate. Magn Reson Imaging 2017; 48:50-61. [PMID: 29278764 DOI: 10.1016/j.mri.2017.12.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 12/09/2017] [Accepted: 12/21/2017] [Indexed: 12/15/2022]
Abstract
PURPOSE To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer. METHODS Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time. RESULTS The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%. CONCLUSION The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method.
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Affiliation(s)
- Soudabeh Kargar
- Biomedical Engineering and Physiology Program, Mayo Graduate School, Rochester, MN, United States; Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Eric A Borisch
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Adam T Froemming
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Akira Kawashima
- Department of Radiology, Mayo Clinic, Scottsdale, AZ, United States
| | - Lance A Mynderse
- Department of Urology, Mayo Clinic, Rochester, MN, United States
| | - Eric G Stinson
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Joshua D Trzasko
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Stephen J Riederer
- Biomedical Engineering and Physiology Program, Mayo Graduate School, Rochester, MN, United States; Department of Radiology, Mayo Clinic, Rochester, MN, United States.
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O'Sullivan F, O'Sullivan JN, Huang J, Doot R, Muzi M, Schubert E, Peterson L, Dunnwald LK, Mankoff DM. Assessment of a statistical AIF extraction method for dynamic PET studies with 15O water and 18F fluorodeoxyglucose in locally advanced breast cancer patients. J Med Imaging (Bellingham) 2017; 5:011010. [PMID: 29201941 PMCID: PMC5700771 DOI: 10.1117/1.jmi.5.1.011010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 10/25/2017] [Indexed: 11/30/2022] Open
Abstract
Blood flow-metabolism mismatch from dynamic positron emission tomography (PET) studies with O15-labeled water (H2O) and F18-labeled fluorodeoxyglucose (FDG) has been shown to be a promising diagnostic for locally advanced breast cancer (LABCa) patients. The mismatch measurement involves kinetic analysis with the arterial blood time course (AIF) as an input function. We evaluate the use of a statistical method for AIF extraction (SAIF) in these studies. Fifty three LABCa patients had dynamic PET studies with H2O and FDG. For each PET study, two AIFs were recovered, an SAIF extraction and also a manual extraction based on a region of interest placed over the left ventricle (LV-ROI). Blood flow-metabolism mismatch was obtained with each AIF, and kinetic and prognostic reliability comparisons were made. Strong correlations were found between kinetic assessments produced by both AIFs. SAIF AIFs retained the full prognostic value, for pathologic response and overall survival, of LV-ROI AIFs.
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Affiliation(s)
| | | | - Jian Huang
- University College Cork, School of Mathematical Sciences, Cork, Ireland
| | - Robert Doot
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Mark Muzi
- University of Washington, Department of Radiology, Seattle, Washington, United States
| | - Erin Schubert
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Lanell Peterson
- University of Washington, Department of Radiology, Seattle, Washington, United States
| | - Lisa K Dunnwald
- University of Iowa, Department of Radiology, Iowa City, Iowa, United States
| | - David M Mankoff
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
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Jones KM, Pagel MD, Cárdenas-Rodríguez J. Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI. Magn Reson Imaging 2017; 47:16-24. [PMID: 29155024 DOI: 10.1016/j.mri.2017.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 10/31/2017] [Accepted: 11/13/2017] [Indexed: 12/27/2022]
Abstract
PURPOSE The purpose of this study was to compare the repeatabilities of the linear and nonlinear Tofts and reference region models (RRM) for dynamic contrast-enhanced MRI (DCE-MRI). MATERIALS AND METHODS Simulated and experimental DCE-MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE-MRI metrics. The quantitative methods used were: 1) linear Tofts model (LTM), 2) non-linear Tofts model (NTM), 3) linear RRM (LRRM), and 4) non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate Ktrans, while LRRM and NRRM were used to estimate Ktrans relative to muscle (RKtrans). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage R&R analysis. RESULTS The iSV for RKtrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally repeatable as Ktrans and RKtrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP. CONCLUSION In simulations and experimental results, linearization improves the repeatability of quantitative DCE-MRI by at least 30%, making it as repeatable as semi-quantitative metrics.
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Affiliation(s)
- Kyle M Jones
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States
| | - Mark D Pagel
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States.
| | - Julio Cárdenas-Rodríguez
- Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States; Department of Medical Imaging, University of Arizona, Tucson, AZ, United States
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Parra NA, Pollack A, Chinea FM, Abramowitz MC, Marples B, Munera F, Castillo R, Kryvenko ON, Punnen S, Stoyanova R. Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness. Front Oncol 2017; 7:259. [PMID: 29177134 PMCID: PMC5686056 DOI: 10.3389/fonc.2017.00259] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 10/16/2017] [Indexed: 12/26/2022] Open
Abstract
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.
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Affiliation(s)
- Nestor Andres Parra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Felix M Chinea
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Brian Marples
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Felipe Munera
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Rosa Castillo
- Department of Radiology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Oleksandr N Kryvenko
- Department of Pathology, University of Miami Miller School of Medicine, Miami, FL, United States.,Department of Urology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Sanoj Punnen
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL, United States
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A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations. Sci Rep 2017; 7:11185. [PMID: 28894197 PMCID: PMC5593829 DOI: 10.1038/s41598-017-11554-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 08/18/2017] [Indexed: 11/15/2022] Open
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.
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58
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Keil VC, Mädler B, Gieseke J, Fimmers R, Hattingen E, Schild HH, Hadizadeh DR. Effects of arterial input function selection on kinetic parameters in brain dynamic contrast-enhanced MRI. Magn Reson Imaging 2017; 40:83-90. [PMID: 28438713 DOI: 10.1016/j.mri.2017.04.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2016] [Revised: 03/20/2017] [Accepted: 04/20/2017] [Indexed: 12/01/2022]
Abstract
PURPOSE Kinetic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) were suggested as a possible instrument for multi-parametric lesion characterization, but have not found their way into clinical practice yet due to inconsistent results. The quantification is heavily influenced by the definition of an appropriate arterial input functions (AIF). Regarding brain tumor DCE-MRI, there are currently several co-existing methods to determine the AIF frequently including different brain vessels as sources. This study quantitatively and qualitatively analyzes the impact of AIF source selection on kinetic parameters derived from commonly selected AIF source vessels compared to a population-based AIF model. MATERIAL AND METHODS 74 patients with brain lesions underwent 3D DCE-MRI. Kinetic parameters [transfer constants of contrast agent efflux and reflux Ktrans and kep and, their ratio, ve, that is used to measure extravascular-extracellular volume fraction and plasma volume fraction vp] were determined using extended Tofts model in 821 ROI from 4 AIF sources [the internal carotid artery (ICA), the closest artery to the lesion, the superior sagittal sinus (SSS), the population-based Parker model]. The effect of AIF source alteration on kinetic parameters was evaluated by tissue type selective intra-class correlation (ICC) and capacity to differentiate gliomas by WHO grade [area under the curve analysis (AUC)]. RESULTS Arterial AIF more often led to implausible ve >100% values (p<0.0001). AIF source alteration rendered different absolute kinetic parameters (p<0.0001), except for kep. ICC between kinetic parameters of different AIF sources and tissues were variable (0.08-0.87) and only consistent >0.5 between arterial AIF derived kinetic parameters. Differentiation between WHO III and II glioma was exclusively possible with vp derived from an AIF in the SSS (p=0.03; AUC 0.74). CONCLUSION The AIF source has a significant impact on absolute kinetic parameters in DCE-MRI, which limits the comparability of kinetic parameters derived from different AIF sources. The effect is also tissue-dependent. The SSS appears to be the best choice for AIF source vessel selection in brain tumor DCE-MRI as it exclusively allowed for WHO grades II/III and III/IV glioma distinction (by vp) and showed the least number of implausible ve values.
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Affiliation(s)
- Vera C Keil
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Burkhard Mädler
- Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany.
| | - Jürgen Gieseke
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany; Philips Healthcare, Röntgenstrasse 22, 22335 Hamburg, Germany.
| | - Rolf Fimmers
- IMBIE (Statistics Department), University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Elke Hattingen
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Hans H Schild
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
| | - Dariusch R Hadizadeh
- Department of Radiology, University Hospital Bonn, Sigmund-Freud-Strasse 25, 53127 Bonn, Germany.
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Hectors SJ, Besa C, Wagner M, Jajamovich GH, Haines GK, Lewis S, Tewari A, Rastinehad A, Huang W, Taouli B. DCE-MRI of the prostate using shutter-speed vs. Tofts model for tumor characterization and assessment of aggressiveness. J Magn Reson Imaging 2017; 46:837-849. [PMID: 28092414 DOI: 10.1002/jmri.25631] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 12/27/2016] [Indexed: 01/10/2023] Open
Abstract
PURPOSE To quantify Tofts model (TM) and shutter-speed model (SSM) perfusion parameters in prostate cancer (PCa) and noncancerous peripheral zone (PZ) and to compare the diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to Prostate Imaging and Reporting and Data System (PI-RADS) classification for the assessment of PCa aggressiveness. MATERIALS AND METHODS Fifty PCa patients (mean age 60 years old) who underwent MRI at 3.0T followed by prostatectomy were included in this Institutional Review Board-approved retrospective study. DCE-MRI parameters (Ktrans , ve , kep [TM&SSM] and intracellular water molecule lifetime τi [SSM]) were determined in PCa and PZ. Differences in DCE-MRI parameters between PCa and PZ, and between models were assessed using Wilcoxon signed-rank tests. Receiver operating characteristic (ROC) analysis for differentiation between PCa and PZ was performed for individual and combined DCE-MRI parameters. Diagnostic performance of DCE-MRI parameters for identification of aggressive PCa (Gleason ≥8, grade group [GG] ≥3 or pathology stage pT3) was assessed using ROC analysis and compared with PI-RADSv2 scores. RESULTS DCE-MRI parameters were significantly different between TM and SSM and between PZ and PCa (P < 0.037). Diagnostic performances of TM and SSM for differentiation of PCa from PZ were similar (highest AUC TM: Ktrans +kep 0.76, SSM: τi +kep 0.80). PI-RADS outperformed TM and SSM DCE-MRI for identification of Gleason ≥8 lesions (AUC PI-RADS: 0.91, highest AUC DCE-MRI: Ktrans +τi SSM 0.61, P = 0.002). The diagnostic performance of PI-RADS and DCE-MRI for identification of GG ≥3 and pT3 PCa was not significantly different (P > 0.213). CONCLUSION SSM DCE-MRI did not increase the diagnostic performance of DCE-MRI for PCa characterization. PI-RADS outperformed both TM and SSM DCE-MRI for identification of aggressive cancer. LEVEL OF EVIDENCE 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:837-849.
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Affiliation(s)
- Stefanie J Hectors
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Cecilia Besa
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mathilde Wagner
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Guido H Jajamovich
- Applied Mathematics and Modeling, Scientific Informatics Department, Merck Sharp & Dohme, Boston, Massachusetts, USA
| | - George K Haines
- Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sara Lewis
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ashutosh Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ardeshir Rastinehad
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Bachir Taouli
- Translational and Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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T2*-Correction in Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Glioblastoma From a Half Dose of High-Relaxivity Contrast Agent. J Comput Assist Tomogr 2017; 41:916-921. [DOI: 10.1097/rct.0000000000000611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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61
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Huang W, Beckett BR, Tudorica A, Meyer JM, Afzal A, Chen Y, Mansoor A, Hayden JB, Doung YC, Hung AY, Holtorf ML, Aston TJ, Ryan CW. Evaluation of Soft Tissue Sarcoma Response to Preoperative Chemoradiotherapy Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging. ACTA ACUST UNITED AC 2016; 2:308-316. [PMID: 28066805 PMCID: PMC5215747 DOI: 10.18383/j.tom.2016.00202] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
This study aims to assess the utility of quantitative dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) parameters in comparison with imaging tumor size for early prediction and evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy. In total, 20 patients with intermediate- to high-grade soft tissue sarcomas received either a phase I trial regimen of sorafenib + chemoradiotherapy (n = 8) or chemoradiotherapy only (n = 12), and underwent DCE-MRI at baseline, after 2 weeks of treatment with sorafenib or after the first chemotherapy cycle, and after therapy completion. MRI tumor size in the longest diameter (LD) was measured according to the RECIST (Response Evaluation Criteria In Solid Tumors) guidelines. Pharmacokinetic analyses of DCE-MRI data were performed using the Shutter-Speed model. After only 2 weeks of treatment with sorafenib or after 1 chemotherapy cycle, Ktrans (rate constant for plasma/interstitium contrast agent transfer) and its percent change were good early predictors of optimal versus suboptimal pathological response with univariate logistic regression C statistics values of 0.90 and 0.80, respectively, whereas RECIST LD percent change was only a fair predictor (C = 0.72). Post-therapy Ktrans, ve (extravascular and extracellular volume fraction), and kep (intravasation rate constant), not RECIST LD, were excellent (C > 0.90) markers of therapy response. Several DCE-MRI parameters before, during, and after therapy showed significant (P < .05) correlations with percent necrosis of resected tumor specimens. In conclusion, absolute values and percent changes of quantitative DCE-MRI parameters provide better early prediction and evaluation of the pathological response of soft tissue sarcoma to preoperative chemoradiotherapy than the conventional measurement of imaging tumor size change.
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Affiliation(s)
- Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon; Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Brooke R Beckett
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Alina Tudorica
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon
| | - Janelle M Meyer
- Division of Hematology and Medical Oncology, Oregon Health & Science University, Portland, Oregon
| | - Aneela Afzal
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon
| | - Yiyi Chen
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon; Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, Oregon
| | - Atiya Mansoor
- Department of Pathology, Oregon Health & Science University, Portland, Oregon
| | - James B Hayden
- Department of Orthopaedics and Rehabilitation, Oregon Health & Science University, Portland, Oregon
| | - Yee-Cheen Doung
- Department of Orthopaedics and Rehabilitation, Oregon Health & Science University, Portland, Oregon
| | - Arthur Y Hung
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon
| | - Megan L Holtorf
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Torrie J Aston
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
| | - Christopher W Ryan
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon; Division of Hematology and Medical Oncology, Oregon Health & Science University, Portland, Oregon
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Farahani K, Kalpathy-Cramer J, Chenevert TL, Rubin DL, Sunderland JJ, Nordstrom RJ, Buatti J, Hylton N. Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network. ACTA ACUST UNITED AC 2016; 2:242-249. [PMID: 28798963 PMCID: PMC5548142 DOI: 10.18383/j.tom.2016.00265] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy.
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Affiliation(s)
- Keyvan Farahani
- Cancer Imaging Program, National Cancer Institute, Bethesda, Maryland
| | | | | | - Daniel L Rubin
- Department of Radiology, Biomedical Data Science, and Medicine (Biomedical Informatics Research), Stanford University, Palo Alto, California
| | | | | | - John Buatti
- Department of Radiation Oncology, University of Iowa, Iowa City, Iowa
| | - Nola Hylton
- Department of Radiology, University of California San Francisco, San Francisco, California
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Ahmed Z, Levesque IR. Increased robustness in reference region model analysis of DCE MRI using two-step constrained approaches. Magn Reson Med 2016; 78:1547-1557. [PMID: 27797110 DOI: 10.1002/mrm.26530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 09/25/2016] [Accepted: 10/06/2016] [Indexed: 12/23/2022]
Abstract
PURPOSE Reference region models (RRMs) can quantify tumor perfusion in dynamic contrast-enhanced MRI without an arterial input function. Inspection of the RRM reveals that one of the free parameters in the fit is uniquely linked to the reference region and is common to all voxels. A two-step approach is proposed that takes this constraint into account. METHODS Three constrained RRM (CRRM) approaches were devised and evaluated. Simulations were performed to compare their accuracy and precision over a range of noise and temporal resolutions. The CRRM was also applied on a virtual phantom that simulates different perfusion values. In vivo evaluation was performed on data from breast cancer and soft tissue sarcoma. RESULTS In simulations, the CRRM consistently improved precision and had better accuracy at low signal-to-noise ratio (SNR). In virtual phantom, the CRRMs were able to fit voxels that had similar kinetics to the reference tissue, whereas the unconstrained models failed to accurately fit these voxels. In the in vivo data, the constrained approaches produced parameter maps that had less variability and were in better agreement with the Tofts model. CONCLUSION These findings indicate that the two-step fitting approach of the CRRM can reduce the variability of perfusion estimates for quantifying perfusion with dynamic contrast-enhanced (DCE) MRI. Magn Reson Med 78:1547-1557, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Zaki Ahmed
- Medical Physics Unit, McGill University, Montreal, QC, Canada
| | - Ives R Levesque
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Research Institute of the McGill University Health Centre, Montreal, QC, Canada
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DCE-MRI Perfusion and Permeability Parameters as predictors of tumor response to CCRT in Patients with locally advanced NSCLC. Sci Rep 2016; 6:35569. [PMID: 27762331 PMCID: PMC5071875 DOI: 10.1038/srep35569] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Accepted: 09/21/2016] [Indexed: 02/06/2023] Open
Abstract
In this prospective study, 36 patients with stage III non-small cell lung cancers (NSCLC), who underwent dynamic contrast-enhanced MRI (DCE-MRI) before concurrent chemo-radiotherapy (CCRT) were enrolled. Pharmacokinetic analysis was carried out after non-rigid motion registration. The perfusion parameters [including Blood Flow (BF), Blood Volume (BV), Mean Transit Time (MTT)] and permeability parameters [including endothelial transfer constant (Ktrans), reflux rate (Kep), fractional extravascular extracellular space volume (Ve), fractional plasma volume (Vp)] were calculated, and their relationship with tumor regression was evaluated. The value of these parameters on predicting responders were calculated by receiver operating characteristic (ROC) curve. Multivariate logistic regression analysis was conducted to find the independent variables. Tumor regression rate is negatively correlated with Ve and its standard variation Ve_SD and positively correlated with Ktrans and Kep. Significant differences between responders and non-responders existed in Ktrans, Kep, Ve, Ve_SD, MTT, BV_SD and MTT_SD (P < 0.05). ROC indicated that Ve < 0.24 gave the largest area under curve of 0.865 to predict responders. Multivariate logistic regression analysis also showed Ve was a significant predictor. Baseline perfusion and permeability parameters calculated from DCE-MRI were seen to be a viable tool for predicting the early treatment response after CCRT of NSCLC.
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Li X, Cai Y, Moloney B, Chen Y, Huang W, Woods M, Coakley FV, Rooney WD, Garzotto MG, Springer CS. Relative sensitivities of DCE-MRI pharmacokinetic parameters to arterial input function (AIF) scaling. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2016; 269:104-112. [PMID: 27288764 PMCID: PMC4958517 DOI: 10.1016/j.jmr.2016.05.018] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Revised: 05/26/2016] [Accepted: 05/27/2016] [Indexed: 05/25/2023]
Abstract
Dynamic-Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has been used widely for clinical applications. Pharmacokinetic modeling of DCE-MRI data that extracts quantitative contrast reagent/tissue-specific model parameters is the most investigated method. One of the primary challenges in pharmacokinetic analysis of DCE-MRI data is accurate and reliable measurement of the arterial input function (AIF), which is the driving force behind all pharmacokinetics. Because of effects such as inflow and partial volume averaging, AIF measured from individual arteries sometimes require amplitude scaling for better representation of the blood contrast reagent (CR) concentration time-courses. Empirical approaches like blinded AIF estimation or reference tissue AIF derivation can be useful and practical, especially when there is no clearly visible blood vessel within the imaging field-of-view (FOV). Similarly, these approaches generally also require magnitude scaling of the derived AIF time-courses. Since the AIF varies among individuals even with the same CR injection protocol and the perfect scaling factor for reconstructing the ground truth AIF often remains unknown, variations in estimated pharmacokinetic parameters due to varying AIF scaling factors are of special interest. In this work, using simulated and real prostate cancer DCE-MRI data, we examined parameter variations associated with AIF scaling. Our results show that, for both the fast-exchange-limit (FXL) Tofts model and the water exchange sensitized fast-exchange-regime (FXR) model, the commonly fitted CR transfer constant (K(trans)) and the extravascular, extracellular volume fraction (ve) scale nearly proportionally with the AIF, whereas the FXR-specific unidirectional cellular water efflux rate constant, kio, and the CR intravasation rate constant, kep, are both AIF scaling insensitive. This indicates that, for DCE-MRI of prostate cancer and possibly other cancers, kio and kep may be more suitable imaging biomarkers for cross-platform, multicenter applications. Data from our limited study cohort show that kio correlates with Gleason scores, suggesting that it may be a useful biomarker for prostate cancer disease progression monitoring.
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Affiliation(s)
- Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States.
| | - Yu Cai
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Brendan Moloney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Yiyi Chen
- Division of Biostatistics, Dept. of Public Health and Preventive Medicine, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, United States
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Mark Woods
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States; Department of Chemistry, Portland State University, Portland, OR 97207, United States
| | - Fergus V Coakley
- Department of Diagnostic Radiology, Oregon Health & Science University, Portland, OR 97239, United States
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States
| | - Mark G Garzotto
- Department of Urology, Oregon Health & Science University, Portland, OR 97239, United States; Portland VA Medical Center, Portland, OR 97239, United States
| | - Charles S Springer
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR 97239, United States
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