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Rajabi P, Rezakhaniha B, Galougahi MHK, Mohammadimehr M, Sharifnia H, Pakzad R, Niroomand H. Unveiling the diagnostic potential of diffusion kurtosis imaging and intravoxel incoherent motion for detecting and characterizing prostate cancer: a meta-analysis. Abdom Radiol (NY) 2025; 50:319-335. [PMID: 39083068 DOI: 10.1007/s00261-024-04454-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 06/08/2024] [Accepted: 06/17/2024] [Indexed: 01/11/2025]
Abstract
PURPOSE This study aims to assess the diagnostic capabilities of Diffusion Kurtosis Imaging (DKI) and Intravoxel Incoherent Motion (IVIM) in prostate cancer (PCa) detection and characterization. MATERIALS A comprehensive search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library for articles published up to September 10, 2023, that evaluated the diagnostic efficacy of MD, MK, Dt, f, and Dp parameters. Data were pooled using a bivariate mixed-effects regression model and analyzed with R software. RESULTS In total, 27 studies were included. The analysis revealed distinct diagnostic efficacies for DKI and IVIM. In the overall model, sensitivity and specificity were 0.807 and 0.797, respectively, with prospective studies showing higher specificity (0.858, p = 0.024). The detection model yielded increased sensitivity (0.845) and specificity (0.812), with DKI outperforming IVIM in both metrics (sensitivity: 0.87, p = 0.043; specificity: 0.837, p = 0.26); MD had high sensitivity (0.88) and specificity (0.82), while MK's specificity was significantly higher (0.854, p = 0.04); Dp's sensitivity was significantly lower (0.64, p = 0.016). In characterization, sensitivity and specificity were 0.708 and 0.735, respectively, with no significant differences between DKI and IVIM or Gleason Scores; MK had higher sensitivity (0.78, p = 0.039), and f's sensitivity was significantly lower (0.51, p = 0.019). CONCLUSION In summary, the study underscores DKI's enhanced diagnostic accuracy over IVIM in detecting PCa, with MK standing out for its precision. Conversely, Dp and f lag in diagnostic performance. Despite these promising results, the study highlights the imperative for standardized protocols and study designs to achieve reliable and consistent outcomes.
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Affiliation(s)
- Pouria Rajabi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Bijan Rezakhaniha
- Department of Urology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | | | - Mojgan Mohammadimehr
- Infectious Diseases Research Center, Aja University of Medical Sciences, Tehran, Iran
- Department of Laboratory Sciences, Faculty of Paramedicine, Aja University of Medical Sciences, Tehran, Iran
| | - Hesam Sharifnia
- Department of Health Management and Economics, School of Medicine, AJA University of Medical Sciences, Tehran, Iran
| | - Roshanak Pakzad
- Department of Otorhinolaryngology-Head and Neck Surgery, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Hassan Niroomand
- Trauma Research Center, AJA University of Medical Sciences, Shahid Etemadzadeh Street, Fatemi Street West, Tehran, Iran.
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Sen S, Singh S, Pye H, Moore CM, Whitaker HC, Punwani S, Atkinson D, Panagiotaki E, Slator PJ. ssVERDICT: Self-supervised VERDICT-MRI for enhanced prostate tumor characterization. Magn Reson Med 2024; 92:2181-2192. [PMID: 38852195 DOI: 10.1002/mrm.30186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 05/17/2024] [Accepted: 05/17/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE Demonstrating and assessing self-supervised machine-learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer. METHODS We derive a self-supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean-squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed-rank test. RESULTS In simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean-squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods. CONCLUSION ssVERDICT significantly outperforms state-of-the-art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.
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Affiliation(s)
- Snigdha Sen
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Hayley Pye
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Caroline M Moore
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Department of Targeted Intervention, Division of Surgery and Interventional Science, University College London, London, UK
| | - Shonit Punwani
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - David Atkinson
- Center for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Eleftheria Panagiotaki
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Paddy J Slator
- Center for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Cardiff University Brain Research Imaging Center, School of Psychology, Cardiff University, Cardiff, UK
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK
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Kallis K, Conlin CC, Ollison C, Hahn ME, Rakow‐Penner R, Dale AM, Seibert TM. Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times. J Appl Clin Med Phys 2024; 25:e14514. [PMID: 39374162 PMCID: PMC11539966 DOI: 10.1002/acm2.14514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 08/08/2024] [Accepted: 08/14/2024] [Indexed: 10/09/2024] Open
Abstract
PURPOSE The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). METHODS This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). RESULTS Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82 ± 1.20SI. After calibration, this bias was reduced to -0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28 ± 2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. CONCLUSIONS The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.
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Affiliation(s)
- Karoline Kallis
- Department of Radiation Medicine and Applied SciencesUC San Diego HealthLa JollaCaliforniaUSA
| | | | - Courtney Ollison
- Department of Radiation Medicine and Applied SciencesUC San Diego HealthLa JollaCaliforniaUSA
| | - Michael E. Hahn
- Department of RadiologyUC San Diego HealthLa JollaCaliforniaUSA
| | | | - Anders M. Dale
- Department of RadiologyUC San Diego HealthLa JollaCaliforniaUSA
- Department of NeurosciencesUC San Diego HealthLa JollaCaliforniaUSA
- Halıcıoğlu Data Science InstituteUC San DiegoLa JollaCaliforniaUSA
| | - Tyler M. Seibert
- Department of Radiation Medicine and Applied SciencesUC San Diego HealthLa JollaCaliforniaUSA
- Department of RadiologyUC San Diego HealthLa JollaCaliforniaUSA
- Department of BioengineeringUC San Diego Jacobs School of EngineeringLa JollaCaliforniaUSA
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Kallis K, Conlin CC, Zhong AY, Hussain TS, Chatterjee A, Karczmar GS, Rakow-Penner R, Dale AM, Seibert TM. Comparison of synthesized and acquired high b-value diffusion-weighted MRI for detection of prostate cancer. Cancer Imaging 2024; 24:89. [PMID: 38972972 PMCID: PMC11229343 DOI: 10.1186/s40644-024-00723-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/10/2024] [Indexed: 07/09/2024] Open
Abstract
BACKGROUND High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. METHODS One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm2 using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm2 via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm2 (sDWI500) and b = 0, b = 500 s/mm2, and b = 1000 s/mm2 (sDWI1000). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). RESULTS Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI1000 and -67 ± 24% for sDWI500. AUC for aDWI, sDWI500, sDWI1000, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. CONCLUSION sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.
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Affiliation(s)
- Karoline Kallis
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Christopher C Conlin
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
| | - Allison Y Zhong
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Troy S Hussain
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA
| | - Aritrick Chatterjee
- Department of Radiology, University of Chicago, Chicago, IL, USA
- Sanford J. Grossmann Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Gregory S Karczmar
- Department of Radiology, University of Chicago, Chicago, IL, USA
- Sanford J. Grossmann Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA
- Department of Neurosciences, University of California San Diego Health, La Jolla, San Diego, CA, USA
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, San Diego, CA, USA
| | - Tyler M Seibert
- Department of Radiology, University of California San Diego Health, La Jolla, San Diego, CA, USA.
- Department of Radiation Medicine and Applied Sciences, University of California San Diego Health, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego Jacobs School of Engineering, La Jolla, San Diego, CA, USA.
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Deforche M, Lefebvre Y, Diamand R, Bali MA, Lemort M, Coquelet N. Improved diagnostic accuracy of readout-segmented echo-planar imaging for peripheral zone clinically significant prostate cancer: a retrospective 3T MRI study. Sci Rep 2024; 14:3299. [PMID: 38332131 PMCID: PMC10853221 DOI: 10.1038/s41598-024-53898-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024] Open
Abstract
This study compares the readout-segmented echo-planar imaging (rsEPI) from the conventional single-shot EPI (ssEPI) diffusion-weighted imaging (DWI) for the discrimination of patients with clinically significant prostate cancer (csPCa) within the peripheral zone (PZ) using apparent diffusion coefficient (ADC) maps and pathology report from magnetic resonance imaging (MRI)-targeted biopsy. We queried a retrospective monocentric database of patients with targeted biopsy. csPCa patients were defined as an International Society of Urological Pathology grade group ≥ 2. Group-level analyses and diagnostic accuracy of mean ADC values (ADCmean) within the tumor volume were assessed from Kruskal-Wallis tests and receiving operating characteristic curves, respectively. Areas under the curve (AUC) and optimal cut-off values were calculated. 159 patients (105 rsEPI, 54 ssEPI; mean age ± standard deviation: 65 ± 8 years) with 3T DWI, PZ lesions and targeted biopsy were selected. Both DWI sequences showed significantly lower ADCmean values for patients with csPCa. The rsEPI sequence better discriminates patients with csPCa (AUCrsEPI = 0.84, AUCssEPI = 0.68, p < 0.05) with an optimal cut-off value of 1232 μm2/s associated with a sensitivity-specificity of 97%-63%. Our study showed that the rsEPI DWI sequence enhances the discrimination of patients with csPCa.
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Affiliation(s)
- M Deforche
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - Y Lefebvre
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - R Diamand
- Urology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, Brussels, Belgium
| | - M A Bali
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - M Lemort
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - N Coquelet
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium.
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Yuan Q, Recchimuzzi DZ, Costa DN. Magnetic Resonance Perfusion Imaging of Prostate. Magn Reson Imaging Clin N Am 2024; 32:171-179. [PMID: 38007279 DOI: 10.1016/j.mric.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
Abstract
Magnetic resonance (MR) perfusion imaging, both with and without exogenous contrast agents, has the potential to assess tissue perfusion and vascularity in prostate cancer. Dynamic contrast-enhanced (DCE) MRI is an important element of the clinical non-invasive multiparametric MRI, which can be used to differentiate benign from malignant lesions, to stage tumors, and to monitor response to therapy. The arterial spin labeled (ASL) and intravoxel incoherent motion (IVIM) diffusion-weighted MRI have the advantage of quantitative perfusion measurements without the concerns of gadolinium-based contrast agent safety and retention issues. The adoption of these non-contrast techniques in clinical practice needs more research and clinical evaluation.
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Affiliation(s)
- Qing Yuan
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA.
| | - Debora Z Recchimuzzi
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA
| | - Daniel N Costa
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX 75390, USA; Department of Urology, University of Texas Southwestern Medical Center, 2201 Inwood Road, TX 75390, USA
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Kallis K, Conlin CC, Ollison C, Hahn ME, Rakow-Penner R, Dale AM, Seibert TM. Quantitative MRI biomarker for classification of clinically significant prostate cancer: calibration for reproducibility across echo times. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.25.24301789. [PMID: 38343810 PMCID: PMC10854339 DOI: 10.1101/2024.01.25.24301789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2024]
Abstract
Background Restriction Spectrum Imaging restriction score (RSIrs) is a quantitative biomarker for detecting clinically significant prostate cancer (csPCa). However, the quantitative value of the RSIrs is affected by imaging parameters such as echo time (TE). Purpose The purpose of the present study is to develop a calibration method to account for differences in echo times and facilitate use of RSIrs as a quantitative biomarker for the detection of csPCa. Methods This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE∼75ms and once at TE=90ms (TEmin 1 , TEmin 2 , and TE90, respectively). A proposed calibration method, trained on patients without csPCa, estimated a linear scaling factor (f) for each of the four diffusion compartments (C) of the RSI signal model. A linear regression model was determined to match C-maps of TE90 to the reference C-maps of TEmin 1 within the interval ranging from 95 th to 99 th percentile of signal intensity within the prostate. RSIrs comparisons were made at 98 th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrs TE90corr ) and uncorrected TE90 (RSIrs TE90 ) to RSIrs from reference TEmin 1 (RSIrs TEmin1 ) and repeated TEmin 2 (RSIrs TEmin2 ). Calibration performance was evaluated with sensitivity, specificity, area under the ROC curve, positive predicted value, negative predicted value, and F1-score. Results Scaling factors for C 1 , C 2 , C 3 , and C 4 were estimated as 1.70, 1.38, 1.03, and 1.19, respectively. In non-csPCa cases, the 98 th percentile of RSIrs TEmin2 and RSIrs TEmin1 differed by 0.27±0.86SI (mean±standard deviation), whereas RSIrs TE90 differed from RSIrs TEmin1 by 1.81±1.20SI. After calibration, this bias was reduced to -0.41±1.20SI, representing a 78% reduction in absolute error. For patients with csPCa, the difference was 0.54±1.98SI between RSIrs TEmin2 and RSIrs TEmin1 and 2.28±2.06SI between RSIrs TE90 and RSIrs TEmin1 . After calibration, the mean difference decreased to -0.86SI, a 38% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrs TEmin1 has a sensitivity of 66% and a specificity of 72%. Prior to calibration, RSIrs TE90 at the same threshold tended to over-diagnose benign cases (sensitivity 44%, specificity 88%). Post-calibration, RSIrs TE90corr performs more similarly to the reference (sensitivity 71%, specificity 62%). Conclusion The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 78% and 38% for non-csPCa and csPCa, respectively.
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Kallis K, Conlin CC, Zhong AY, Hussain TS, Chatterjee A, Karczmar GS, Rakow-Penner R, Dale A, Seibert T. Comparison of synthesized and acquired high b -value diffusion-weighted MRI for detection of prostate cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.17.23286100. [PMID: 36824958 PMCID: PMC9949172 DOI: 10.1101/2023.02.17.23286100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2023]
Abstract
Background High b -value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). To decrease scan time and improve signal-to-noise ratio, high b -value (>1000 s/mm 2 ) images are often synthesized instead of acquired. Purpose Qualitatively and quantitatively compare synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa. Study Type Retrospective. Subjects 151 consecutive patients who underwent prostate MRI and biopsy. Sequence Axial DWI with b =0, 500, 1000, and 2000 s/mm 2 using a 3T clinical scanner using a 32-channel phased-array body coil. Assessment We synthesized DWI for b =2000 s/mm 2 via extrapolation based on monoexponential decay, using b =0 and b =500 s/mm 2 (sDWI 500 ) and b =0, b =500, and b =1000 s/mm 2 (sDWI 1000 ). Differences between sDWI and aDWI were evaluated within regions of interest (ROIs). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was also compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Statistical Tests Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC). Statistical significance was assessed using bootstrap difference (two-sided α=0.05). Results Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46±35% for sDWI 1000 and -67±24% for sDWI 500 . AUC for aDWI, sDWI 500, sDWI 1000 , and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively. When considering the whole field of view, classification accuracy and qualitative image quality decreased notably for sDWI compared to aDWI and RSIrs. Data Conclusion sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.
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Qin Y, Chen C, Chen H, Gao F. The value of intravoxel incoherent motion model-based diffusion-weighted imaging for predicting long-term outcomes in nasopharyngeal carcinoma. Front Oncol 2022; 12:902819. [DOI: 10.3389/fonc.2022.902819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 11/03/2022] [Indexed: 12/04/2022] Open
Abstract
ObjectiveThe aim of this study was to evaluate the prognostic value for survival of parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in patients with nasopharyngeal carcinoma (NPC).MaterialsBaseline IVIM-DWI was performed on 97 newly diagnosed NPC patients in this prospective study. The relationships between the pretreatment IVIM-DWI parametric values (apparent diffusion coefficient (ADC), D, D*, and f) of the primary tumors and the patients’ 3-year survival were analyzed in 97 NPC patients who received chemoradiotherapy. The cutoff values of IVIM parameters for local relapse-free survival (LRFS) were identified by a non-parametric log-rank test. The local-regional relapse-free survival (LRRFS), LRFS, regional relapse-free survival (RRFS), distant metastasis-free survival (DMFS), progression-free survival (PFS), and overall survival (OS) rates were calculated by using the Kaplan–Meier method. A Cox proportional hazards model was used to explore the independent predictors for prognosis.ResultsThere were 97 participants (mean age, 48.4 ± 10.5 years; 65 men) analyzed. Non-parametric log-rank test results showed that the optimal cutoff values of ADC, D, D*, and f were 0.897 × 10−3 mm2/s, 0.699 × 10−3 mm2/s, 8.71 × 10−3 mm2/s, and 0.198%, respectively. According to the univariable analysis, the higher ADC group demonstrated significantly higher OS rates than the low ADC group (p = 0.036), the higher D group showed significantly higher LRFS and OS rates than the low D group (p = 0.028 and p = 0.017, respectively), and the higher D* group exhibited significantly higher LRFS and OS rates than the lower D* group (p = 0.001 and p = 0.002, respectively). Multivariable analyses indicated that ADC and D were the independent prognostic factors for LRFS (p = 0.041 and p = 0.037, respectively), D was an independent prognostic factor for LRRFS (p = 0.045), D* and f were the independent prognostic factors for OS (p = 0.019 and 0.029, respectively), and f acted was an independent prognostic factor for DMFS (p = 0.020).ConclusionsBaseline IVIM-DWI perfusion parameters ADC and D, together with diffusion parameter D*, could act as useful factors for predicting long-term outcomes and selecting high-risk patients with NPC.
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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11
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Sen S, Valindria V, Slator PJ, Pye H, Grey A, Freeman A, Moore C, Whitaker H, Punwani S, Singh S, Panagiotaki E. Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models. Diagnostics (Basel) 2022; 12:1631. [PMID: 35885536 PMCID: PMC9319485 DOI: 10.3390/diagnostics12071631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/29/2022] [Accepted: 07/02/2022] [Indexed: 11/16/2022] Open
Abstract
False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.
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Affiliation(s)
- Snigdha Sen
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK; (S.S.); (V.V.); (P.J.S.)
| | - Vanya Valindria
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK; (S.S.); (V.V.); (P.J.S.)
| | - Paddy J. Slator
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK; (S.S.); (V.V.); (P.J.S.)
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, University College London, London WC1E 6BT, UK; (H.P.); (H.W.)
| | - Alistair Grey
- Department of Urology, University College London Hospitals NHS Foundations Trust, London NW1 2PG, UK; (A.G.); (C.M.)
| | - Alex Freeman
- Department of Pathology, University College London Hospitals NHS Foundations Trust, London NW1 2PG, UK;
| | - Caroline Moore
- Department of Urology, University College London Hospitals NHS Foundations Trust, London NW1 2PG, UK; (A.G.); (C.M.)
| | - Hayley Whitaker
- Molecular Diagnostics and Therapeutics Group, University College London, London WC1E 6BT, UK; (H.P.); (H.W.)
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London WC1E 6BT, UK; (S.P.); (S.S.)
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London WC1E 6BT, UK; (S.P.); (S.S.)
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1E 6BT, UK; (S.S.); (V.V.); (P.J.S.)
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12
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Hoang-Dinh A, Nguyen-Quang T, Bui-Van L, Gonindard-Melodelima C, Souchon R, Rouvière O. Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI. Diagn Interv Imaging 2022; 103:545-554. [PMID: 35773099 DOI: 10.1016/j.diii.2022.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 11/17/2022]
Abstract
PURPOSE The purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ). MATERIALS AND METHODS Fifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference. RESULTS Forty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21-37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10-6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0-500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10-6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10-6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10-6 to 311 × 10-6 mm²/s across scanners, b-value combinations and periods of the day. CONCLUSIONS The MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.
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Affiliation(s)
- Au Hoang-Dinh
- Hanoï Medical University Hospital, Dong Da, Hanoi, Viet Nam
| | | | - Lenh Bui-Van
- Hanoï Medical University Hospital, Dong Da, Hanoi, Viet Nam
| | | | | | - Olivier Rouvière
- LabTAU, INSERM, U1032, 69000, Lyon, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, 69000, Lyon, France; Université de Lyon, Lyon 69003, France; Université Lyon 1, Lyon France; Faculté de Médecine, Lyon Est, 69003, Lyon, France.
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13
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Alkadi R, Abdullah O, Werghi N. The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study. J Digit Imaging 2022; 35:678-691. [PMID: 35182292 PMCID: PMC9156596 DOI: 10.1007/s10278-022-00604-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 12/15/2022] Open
Abstract
This study aims at investigating the classification power of different b-values of the diffusion-weighted magnetic resonance images (DWI) as indicator of prostate cancer. This paper investigates several techniques for analyzing data from DWI acquired at a range of b-values for the purpose of detecting prostate cancer. In the first phase of experiments, we analyze the available data by producing two main parametric maps using two common models, namely: the intra-voxel incoherent motion (IVIM) model and the mono-exponential ADC model. Accordingly, we evaluated the benign/malignant tissue classification potential of several parametric maps produced using different combinations of b-values and fitting models. In the second phase, we utilized the maps that performed best in the first phase of experiments to design a machine learning-based computer-assisted diagnosis system for the detection of early stage prostate cancer. The system performance was cross-validated using data from 20 patients. On a fivefold cross-validation scheme, a maximum accuracy and an area under the receiver operating characteristic (AUC) of 90% and 0.978, respectively, was achieved by a system that uses ADC maps fitted using the mono-exponential model at 11 different b-values. The results suggest that the proposed machine learning-based diagnosis system is potentially powerful in differentiating between malignant and benign prostate tissues when combined with carefully generated ADC maps.
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14
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Gao F, Zhao W, Zheng Y, Duan Y, Ji M, Lin G, Zhu Z. Intravoxel Incoherent Motion Magnetic Resonance Imaging Used in Preoperative Screening of High-Risk Patients With Moyamoya Disease Who May Develop Postoperative Cerebral Hyperperfusion Syndrome. Front Neurosci 2022; 16:826021. [PMID: 35310102 PMCID: PMC8924456 DOI: 10.3389/fnins.2022.826021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/26/2022] [Indexed: 11/16/2022] Open
Abstract
Objective This study aimed to investigate the feasibility of preoperative intravoxel incoherent motion (IVIM) MRI for the screening of high-risk patients with moyamoya disease (MMD) who may develop postoperative cerebral hyperperfusion syndrome (CHS). Methods This study composed of two parts. In the first part 24 MMD patients and 24 control volunteers were enrolled. IVIM-MRI was performed. The relative pseudo-diffusion coefficient, perfusion fraction, apparent diffusion coefficient, and diffusion coefficient (rD*, rf, rADC, and rD) values of the IVIM sequence were compared according to hemispheres between MMD patient and healthy control groups. In the second part, 98 adult patients (124 operated hemispheres) with MMD who underwent surgery were included. Preoperative IVIM-MRI was performed. The rD*, rf, rADC, rD, and rfD* values of the IVIM sequence were calculated and analyzed. Operated hemispheres were divided into CHS and non-CHS groups. Patients’ age, sex, Matsushima type, Suzuki stage, and IVIM-MRI examination results were compared between CHS and non-CHS groups. Results Only the rf value was significantly higher in the healthy control group than in the MMD group (P < 0.05). Out of 124 operated hemispheres, 27 were assigned to the CHS group. Patients with clinical presentation of Matsushima types I–V were more likely to develop CHS after surgery (P < 0.05). The rf values of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). The rfD* values of the ACA and MCA supply areas of the ipsilateral hemisphere were significantly higher in the CHS group than in the non-CHS group (P < 0.05). Only the rf value of the anterior cerebral artery supply area in the contralateral hemisphere was higher in the CHS group than in the non-CHS group (P < 0.05). The rf values of the middle and posterior cerebral artery supply areas and the rD, rD*, and rADC values of the both hemispheres were not significantly different between the CHS and non-CHS groups (P > 0.05). Conclusion Preoperative non-invasive IVIM-MRI analysis, particularly the f-value of the ipsilateral hemisphere, may be helpful in predicting CHS in adult patients with MMD after surgery. MMD patients with ischemic onset symptoms are more likely to develop CHS after surgery.
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Affiliation(s)
- Feng Gao
- Department of Radiology, Huadong Hospital Fudan University, Shanghai, China
- *Correspondence: Feng Gao,
| | - Wei Zhao
- Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yu Zheng
- Department of Radiology, Chengdu Second People’s Hospital, Chengdu, China
| | - Yu Duan
- Department of Neurosurgery, Huadong Hospital Fudan University, Shanghai, China
| | - Ming Ji
- Department of Radiology, Huadong Hospital Fudan University, Shanghai, China
| | - Guangwu Lin
- Department of Radiology, Huadong Hospital Fudan University, Shanghai, China
| | - Zhenfang Zhu
- Department of Radiology, Huadong Hospital Fudan University, Shanghai, China
- Zhenfang Zhu,
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15
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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16
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Wang X, Hielscher T, Radtke JP, Görtz M, Schütz V, Kuder TA, Gnirs R, Schwab C, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D. Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically significant prostate cancer. Eur J Radiol 2021; 136:109538. [PMID: 33482592 DOI: 10.1016/j.ejrad.2021.109538] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/28/2020] [Accepted: 01/07/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Mean ADC has high predictive value for the presence of clinically significant prostate cancer (sPC). Measurement variability is introduced by different scanners, protocols, intra-and inter-patient variation. Internal calibration by ADC ratios can address such fluctuations however can potentially lower the biological value of quantitative ADC determination by being sensitive to deviations in reference tissue signal. PURPOSE To better understand the predictive value of quantitative ADC measurements in comparison to internal reference ratios when measured in a single scanner, single protocol setup. MATERIALS AND METHODS 284 consecutive patients who underwent 3 T MRI on a single scanner followed by MRI-transrectal ultrasound fusion biopsy were included. A board-certified radiologist retrospectively reviewed all MRIs blinded to clinical information and placed regions of interest (ROI) on all focal lesions and the following reference regions: normal-appearing peripheral zone (PZNL) and transition zone (TZNL), the urinary bladder (BLA), and right and left internal obturator muscle (RIOM, LIOM). ROI-based mean ADC and ADC ratios to the reference regions were compared regarding their ability to predict the aggressiveness of prostate cancer. Spearman's rank correlation coefficient was used to estimate the correlation between ADC parameters, Gleason score (GS) and ADC ratios. The primary endpoint was presence of sPC, defined as a GS ≥ 3 + 4. Univariable and multivariable logistic regression models were constructed to predict sPC. Receiver operating characteristics curves (ROC) were used for visualization; DeLong test was used to evaluate the differences of the area under the curve (AUC). Bias-corrected AUC values and corresponding 95 %-CI were calculated using bootstrapping with 100 bootstrap samples. RESULTS After exclusion of patients who received prior treatment, 259 patients were included in the final cohort of which 220 harbored 351 MR lesions. Mean ADC and ADC ratios demonstrated a negative correlation with the GS. Mean ADC had the strongest correlation with ρ of -0.34, followed by ADCratioPZNL (ρ=-0.32). All ADC parameters except ADCratioLIOM (p = 0.07) were associated with sPC p<0.05). Mean ADC and ADCratioPZNL had the highest ROC AUC of all parameters (0.68). Multivariable models with mean ADC improve predictive performance. CONCLUSIONS A highly standardized single-scanner mean ADC measurement could not be improved upon using any of the single ADC ratio parameters or combinations of these parameters in predicting the aggressiveness of prostate cancer.
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Affiliation(s)
- Xianfeng Wang
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiology, Affiliated Hospital of Guilin Medical University, Guangxi, Guilin, PR China
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Philipp Radtke
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Tristan Anselm Kuder
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Regula Gnirs
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Constantin Schwab
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany.
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Kooreman ES, van Houdt PJ, Keesman R, Pos FJ, van Pelt VWJ, Nowee ME, Wetscherek A, Tijssen RHN, Philippens MEP, Thorwarth D, Wang J, Shukla-Dave A, Hall WA, Paulson ES, van der Heide UA. ADC measurements on the Unity MR-linac - A recommendation on behalf of the Elekta Unity MR-linac consortium. Radiother Oncol 2020; 153:106-113. [PMID: 33017604 PMCID: PMC8327388 DOI: 10.1016/j.radonc.2020.09.046] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/23/2020] [Accepted: 09/23/2020] [Indexed: 01/23/2023]
Abstract
BACKGROUND AND PURPOSE Diffusion-weighted imaging (DWI) for treatment response monitoring is feasible on hybrid magnetic resonance linear accelerator (MR-linac) systems. The MRI scanner of the Elekta Unity system has an adjusted design compared to diagnostic scanners. We investigated its impact on measuring the DWI-derived apparent diffusion coefficient (ADC) regarding three aspects: the choice of b-values, the spatial variation of the ADC, and scanning during radiation treatment. The aim of this study is to give recommendations for accurate ADC measurements on Unity systems. MATERIALS AND METHODS Signal-to-noise ratio (SNR) measurements with increasing b-values were done to determine the highest bvalue that can be measured reliably. The spatial variation of the ADC was assessed on six Unity systems with a cylindrical phantom of 40 cm diameter. The influence of gantry rotation and irradiation was investigated by acquiring DWI images before and during treatment of 11 prostate cancer patients. RESULTS On the Unity system, a maximum b-value of 500 s/mm2 should be used for ADC quantification, as a trade-off between SNR and diffusion weighting. Accurate ADC values were obtained within 7 cm from the iso-center, while outside this region ADC values deviated more than 5%. The ADC was not influenced by the rotating linac or irradiation during treatment. CONCLUSION We provide Unity system specific recommendations for measuring the ADC. This will increase the consistency of ADC values acquired in different centers on the Unity system, enabling large cohort studies for biomarker discovery and treatment response monitoring.
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Affiliation(s)
- Ernst S Kooreman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Rick Keesman
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Floris J Pos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Vivian W J van Pelt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Marlies E Nowee
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research, and The Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Rob H N Tijssen
- Department of Radiotherapy, University Medical Center Utrecht, The Netherlands
| | | | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Jihong Wang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, United States
| | - Amita Shukla-Dave
- Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, United States
| | - William A Hall
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, United States
| | - Eric S Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, United States
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
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18
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He N, Li Z, Li X, Dai W, Peng C, Wu Y, Huang H, Liang J. Intravoxel Incoherent Motion Diffusion-Weighted Imaging Used to Detect Prostate Cancer and Stratify Tumor Grade: A Meta-Analysis. Front Oncol 2020; 10:1623. [PMID: 33042805 PMCID: PMC7518084 DOI: 10.3389/fonc.2020.01623] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/27/2020] [Indexed: 12/17/2022] Open
Abstract
Objectives: Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is a promising non-invasive imaging technique to detect and grade prostate cancer (PCa). However, the results regarding the diagnostic performance of IVIM-DWI in the characterization and classification of PCa have been inconsistent among published studies. This meta-analysis was performed to summarize the diagnostic performance of IVIM-DWI in the differential diagnosis of PCa from non-cancerous tissues and to stratify the tumor Gleason grades in PCa. Materials and Methods: Studies concerning the differential diagnosis of prostate lesions using IVIM-DWI were systemically searched in PubMed, Embase, and Web of Science without time limitation. Review Manager 5.3 was used to calculate the standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D*), and perfusion fraction (f). Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as publication bias and heterogeneity. Fagan's nomogram was used to predict the post-test probabilities. Results: Twenty studies with 854 patients confirmed with PCa were included. Most of the included studies showed a low to unclear risk of bias and low concerns regarding applicability. PCa showed a significantly lower ADC (SMD = −2.34; P < 0.001) and D values (SMD = −1.86; P < 0.001) and a higher D* value (SMD = 0.29; P = 0.01) than non-cancerous tissues, but no difference was noted with the f value (SMD = −0.16; P = 0.50). Low-grade PCa showed higher ADC (SMD = 0.63; P < 0.001) and D values (SMD = 0.80; P < 0.001) than the high-grade lesions. ADC showed comparable diagnostic performance (sensitivity = 86%; specificity = 86%; AUC = 0.87) but higher post-test probabilities (60, 53, 36, and 36% for ADC, D, D*, and f values, respectively) compared with the D (sensitivity = 82%; specificity = 82%; AUC = 0.85), D* (sensitivity = 70%; specificity = 70%; AUC = 0.75), and f values (sensitivity = 73%; specificity = 68%; AUC = 0.76). Conclusion: IVIM parameters are adequate to differentiate PCa from non-cancerous tissues with good diagnostic performance but are not superior to the ADC value. Diffusion coefficients can further stratify the tumor Gleason grades in PCa.
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Affiliation(s)
- Ni He
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Zhipeng Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Xie Li
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Wei Dai
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Chuan Peng
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yaopan Wu
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Haitao Huang
- Department of Radiology, Maoming People's Hospital, Maoming, China
| | - Jianye Liang
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.,Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China
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Goodburn RJ, Barrett T, Patterson I, Gallagher FA, Lawrence EM, Gnanapragasam VJ, Kastner C, Priest AN. Removing rician bias in diffusional kurtosis of the prostate using real-data reconstruction. Magn Reson Med 2020; 83:2243-2252. [PMID: 31737935 PMCID: PMC7065237 DOI: 10.1002/mrm.28080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 10/22/2019] [Accepted: 10/23/2019] [Indexed: 12/24/2022]
Abstract
PURPOSE To compare prostate diffusional kurtosis imaging (DKI) metrics generated using phase-corrected real data with those generated using magnitude data with and without noise compensation (NC). METHODS Diffusion-weighted images were acquired at 3T in 16 prostate cancer patients, measuring 6 b-values (0-1500 s/mm2 ), each acquired with 6 signal averages along 3 diffusion directions, with noise-only images acquired to allow NC. In addition to conventional magnitude averaging, phase-corrected real data were averaged in an attempt to reduce rician noise-bias, with a range of phase-correction low-pass filter (LPF) sizes (8-128 pixels) tested. Each method was also tested using simulations. Pixelwise maps of apparent diffusion (D) and apparent kurtosis (K) were calculated for magnitude data with and without NC and phase-corrected real data. Average values were compared in tumor, normal transition zone (NTZ), and normal peripheral zone (NPZ). RESULTS Simulations indicated LPF size can strongly affect K metrics, where 64-pixel LPFs produced accurate metrics. Relative to metrics estimated from magnitude data without NC, median NC K were lower (P < 0.0001) by 6/11/8% in tumor/NPZ/NTZ, 64-LPF real-data K were lower (P < 0.0001) by 4/10/7%, respectively. CONCLUSION Compared with magnitude data with NC, phase-corrected real data can produce similar K, although the choice of phase-correction LPF should be chosen carefully.
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Affiliation(s)
- Rosie J. Goodburn
- Department of Medical PhysicsCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
- Division of Radiotherapy and ImagingThe Institute of Cancer ResearchLondon
| | - Tristan Barrett
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Ilse Patterson
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Ferdia A. Gallagher
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | - Edward M. Lawrence
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Christof Kastner
- Department of UrologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
| | - Andrew N. Priest
- Department of RadiologySchool of Clinical MedicineUniversity of CambridgeCambridgeUnited Kingdom
- Department of RadiologyCambridge University Hospitals NHS Foundation TrustCambridgeUnited Kingdom
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20
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Ștefan PA, Csutak C, Lebovici A, Rusu GM, Mihu CM. Diffusion-Weighted Magnetic Resonance Imaging as a Noninvasive Parameter for Differentiating Benign and Malignant Intraperitoneal Collections. ACTA ACUST UNITED AC 2020; 56:medicina56050217. [PMID: 32369983 PMCID: PMC7279298 DOI: 10.3390/medicina56050217] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/26/2020] [Accepted: 04/29/2020] [Indexed: 12/15/2022]
Abstract
Background and Objective: The imaging differentiation of benign from malignant intraperitoneal collections (IPCs) relies on the tumoral morphological modifications of the peritoneum, which are not always advocating for malignancy. We aimed to assess ascitic fluid with the apparent diffusion coefficient (ADC) to determine non-invasive, stand-alone, differentiation criteria for benign and malignant intraperitoneal effusions. Materials and Methods: Sixty-one patients with known IPCs who underwent magnetic resonance examinations for reasons such as tumor staging, undetermined abdominal mass and disease follow up were retrospectively included in this study. All subjects had a final diagnosis of the fluid based on pathological examinations, which were divided into benign (n = 37) and malignant (n = 24) IPCs groups. ADC values were measured separately by two radiologists, and the average values were used for comparing the two groups by consuming the independent samples t-test. The receiver operating characteristic analysis was performed to test the ADC values' diagnostic ability to distinguish malignant from benign collections. Results: The differentiation between benign and malignant IPCs based on ADC values was statistically significant (p = 0.0034). The mean ADC values were higher for the benign (3.543 × 10-3 mm2/s) than for the malignant group (3.057 × 10-3 mm2/s). The optimum ADC cutoff point for the diagnosis of malignant ascites was <3.241 × 10-3 mm2/s, with a sensitivity of 77.78% and a specificity of 80%. Conclusions: ADC represents a noninvasive and reproducible imaging parameter that may help to assess intraperitoneal collections. Although successful in distinguishing malignant from benign IPCs, further research must be conducted in order to certify if the difference in ADC values is a consequence of the physical characteristics of the ascitic fluids or their appurtenance to a certain histopathological group.
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Affiliation(s)
- Paul-Andrei Ștefan
- Anatomy and Embryology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania;
- Radiology and Imaging Department, County Emergency Hospital, 400012 Cluj-Napoca, Romania; (A.L.); (G.M.R.); (C.M.M.)
| | - Csaba Csutak
- Radiology and Imaging Department, County Emergency Hospital, 400012 Cluj-Napoca, Romania; (A.L.); (G.M.R.); (C.M.M.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
- Correspondence: ; Tel.: +40-7-4564-2495
| | - Andrei Lebovici
- Radiology and Imaging Department, County Emergency Hospital, 400012 Cluj-Napoca, Romania; (A.L.); (G.M.R.); (C.M.M.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Georgeta Mihaela Rusu
- Radiology and Imaging Department, County Emergency Hospital, 400012 Cluj-Napoca, Romania; (A.L.); (G.M.R.); (C.M.M.)
- Radiology, Surgical Specialties Department, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
| | - Carmen Mihaela Mihu
- Radiology and Imaging Department, County Emergency Hospital, 400012 Cluj-Napoca, Romania; (A.L.); (G.M.R.); (C.M.M.)
- Histology, Morphological Sciences Department, “Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, 400012 Cluj-Napoca, Romania
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21
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Núñez DA, Lu Y, Paudyal R, Hatzoglou V, Moreira AL, Oh JH, Stambuk HE, Mazaheri Y, Gonen M, Ghossein RA, Shaha AR, Tuttle RM, Shukla-Dave A. Quantitative Non-Gaussian Intravoxel Incoherent Motion Diffusion-Weighted Imaging Metrics and Surgical Pathology for Stratifying Tumor Aggressiveness in Papillary Thyroid Carcinomas. ACTA ACUST UNITED AC 2020; 5:26-35. [PMID: 30854439 PMCID: PMC6403039 DOI: 10.18383/j.tom.2018.00054] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
We assessed a priori aggressive features using quantitative diffusion-weighted imaging metrics to preclude an active surveillance management approach in patients with papillary thyroid cancer (PTC) with tumor size 1-2 cm. This prospective study enrolled 24 patients with PTC who underwent pretreatment multi-b-value diffusion-weighted imaging on a GE 3 T magnetic resonance imaging scanner. The apparent diffusion coefficient (ADC) metric was calculated from monoexponential model, and the perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), and diffusion kurtosis coefficient (K) metrics were estimated using the non-Gaussian intravoxel incoherent motion model. Neck ultrasonography examination data were used to calculate tumor size. The receiver operating characteristic curve assessed the discriminative specificity, sensitivity, and accuracy between PTCs with and without features of tumor aggressiveness. Multivariate logistic regression analysis was performed on metrics using a leave-1-out cross-validation method. Tumor aggressiveness was defined by surgical histopathology. Tumors with aggressive features had significantly lower ADC and D values than tumors without tumor-aggressive features (P < .05). The absolute relative change was 46% in K metric value between the 2 tumor types. In total, 14 patients were in the critical size range (1-2 cm) measured by ultrasonography, and the ADC and D were significantly different and able to differentiate between the 2 tumor types (P < .05). ADC and D can distinguish tumors with aggressive histological features to preclude an active surveillance management approach in patients with PTC with tumors measuring 1-2 cm.
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Affiliation(s)
- David Aramburu Núñez
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yonggang Lu
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Andre L Moreira
- Department of Pathology, NYU Langone Medical Center, New York, NY
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | | | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY.,Departments of Radiology
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22
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Brancato V, Cavaliere C, Salvatore M, Monti S. Non-Gaussian models of diffusion weighted imaging for detection and characterization of prostate cancer: a systematic review and meta-analysis. Sci Rep 2019; 9:16837. [PMID: 31728007 PMCID: PMC6856159 DOI: 10.1038/s41598-019-53350-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022] Open
Abstract
The importance of Diffusion Weighted Imaging (DWI) in prostate cancer (PCa) diagnosis have been widely handled in literature. In the last decade, due to the mono-exponential model limitations, several studies investigated non-Gaussian DWI models and their utility in PCa diagnosis. Since their results were often inconsistent and conflicting, we performed a systematic review of studies from 2012 examining the most commonly used Non-Gaussian DWI models for PCa detection and characterization. A meta-analysis was conducted to assess the ability of each Non-Gaussian model to detect PCa lesions and distinguish between low and intermediate/high grade lesions. Weighted mean differences and 95% confidence intervals were calculated and the heterogeneity was estimated using the I2 statistic. 29 studies were selected for the systematic review, whose results showed inconsistence and an unclear idea about the actual usefulness and the added value of the Non-Gaussian model parameters. 12 studies were considered in the meta-analyses, which showed statistical significance for several non-Gaussian parameters for PCa detection, and to a lesser extent for PCa characterization. Our findings showed that Non-Gaussian model parameters may potentially play a role in the detection and characterization of PCa but further studies are required to identify a standardized DWI acquisition protocol for PCa diagnosis.
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23
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Kamata Y, Shinohara Y, Kuya K, Tsubouchi Y, Saito Y, Maegaki Y, Fujii S, Ogawa T. Computed diffusion-weighted imaging for acute pediatric encephalitis/encephalopathy. Acta Radiol 2019; 60:1341-1347. [PMID: 30674215 DOI: 10.1177/0284185118823335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Yuji Kamata
- Division of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Yuki Shinohara
- Division of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Keita Kuya
- Division of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Yoshiko Tsubouchi
- Division of Child Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Yoshiaki Saito
- Division of Child Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Yoshihiro Maegaki
- Division of Child Neurology, Department of Brain and Neurosciences, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Shinya Fujii
- Division of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Toshihide Ogawa
- Division of Radiology, Department of Pathophysiological and Therapeutic Science, Faculty of Medicine, Tottori University, Yonago, Japan
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Wu D, Zhang J. Evidence of the diffusion time dependence of intravoxel incoherent motion in the brain. Magn Reson Med 2019; 82:2225-2235. [PMID: 31267578 DOI: 10.1002/mrm.27879] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 05/31/2019] [Accepted: 06/01/2019] [Indexed: 12/17/2022]
Abstract
PURPOSE To investigate the diffusion time (TD ) dependence of intravoxel incoherent motion (IVIM) signals in the brain. METHODS A 3-compartment IVIM model was proposed to characterize 2 types of microcirculatory flows in addition to tissue water in the brain: flows that cross multiple vascular segments (pseudo-diffusive) and flows that stay in 1 segment (ballistic) within TD . The model was first evaluated using simulated flow signals. Experimentally, flow-compensated (FC) pulsed-gradient spin-echo (PGSE) and oscillating-gradient spin-echo (OGSE) sequences were tested using a flow phantom and then used to examine IVIM signals in the mouse brain with TD ranging from ~2.5 ms to 40 ms on an 11.7T scanner. RESULTS By fitting the model to simulated flow signals, we demonstrated the TD dependency of the estimated fraction of pseudo-diffusive flow and the pseudo-diffusion coefficient (D*), which were dictated by the characteristic timescale of microcirculatory flow (τ). Flow phantom experiments validated that the OGSE and FC-PGSE sequences were not susceptible to the change in flow velocity. In vivo mouse brain data showed that both the estimated fraction of pseudo-diffusive flow and D* increased significantly as TD increased. CONCLUSION We demonstrated that IVIM signals measured in the brain are TD -dependent, potentially because more microcirculatory flows approach the pseudo-diffusive limit as TD increases with respect to τ. Measuring the TD dependency of IVIM signals may provide additional information on microvascular flows in the brain.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiangyang Zhang
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York
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25
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Borlinhas F, Loução R, C Conceição R, Ferreira HA. Gamma Distribution Model in the Evaluation of Breast Cancer Through Diffusion-Weighted MRI: A Preliminary Study. J Magn Reson Imaging 2018; 50:230-238. [PMID: 30589146 DOI: 10.1002/jmri.26599] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 11/21/2018] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The gamma distribution (GD) model is based on the statistical distribution of the apparent diffusion coefficient (ADC) parameter. The GD model is expected to reflect the probability of the distribution of water molecule mobility in different regions of tissue, but also the intra- and extracellular diffusion and perfusion components (f1 , f2 , f3 fractions). PURPOSE To assess the GD model in the characterization and diagnostic performance of breast lesions. STUDY TYPE Prospective. POPULATION In all, 48 females with 24 benign and 33 malignant breast lesions. FIELD STRENGTH/SEQUENCE A diffusion-weighted sequence (b = 0-3000 s/mm2 ) with a 3 T scanner. ASSESSMENT For each group of benign, malignant, invasive, and in situ breast lesions, the ADC was obtained. Also, θ and k parameters (scale and shape of the statistic distribution, respectively), f1 , f2 , and f3 fractions were obtained from fitting the GD model to diffusion data. STATISTICAL TESTS Lesion types were compared regarding diffusion parameters using nonparametric statistics and receiver operating characteristic curve diagnostic performance. RESULTS The majority of GD parameters (k, f1 , f2 , f3 fractions) showed significant differences between benign and malignant lesions, and between in situ and invasive lesions (f1 , f2 , f3 fractions) (P ≤ 0.001). The best diagnostic performances were obtained with ADC and f1 fraction in benign vs. malignant lesions (area under curve [AUC] = 0.923 and 0.913, sensitivity = 93.9% and 81.8%, specificity = 79.2% and 91.7%, accuracy = 87.7% and 86.0%, respectively). In invasive lesions vs. in situ lesions, the best diagnostic performance was obtained with f1 fraction, which outperformed ADC results (AUC = 0.978 and 0.941, and sensitivity = 91.3% for both parameters, specificity = 100.0% and 90.0%, accuracy = 93.9% and 90.9%, respectively). DATA CONCLUSION This work shows that the GD model provides information in addition to the ADC parameter, suggesting its potential in the diagnosis of breast lesions. Level of Evidence 2: Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:230-238.
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Affiliation(s)
- Filipa Borlinhas
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Ricardo Loução
- Institute of Neuroscience and Medicine (INM - 4), Forschungszentrum Jülich, Jülich, Germany
| | - Raquel C Conceição
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Hugo A Ferreira
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
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26
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Mazaheri Y, Shukla-Dave A, Goldman DA, Moskowitz CS, Takeda T, Reuter VE, Akin O, Hricak H. Characterization of prostate cancer with MR spectroscopic imaging and diffusion-weighted imaging at 3 Tesla. Magn Reson Imaging 2018; 55:93-102. [PMID: 30176373 DOI: 10.1016/j.mri.2018.08.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE To retrospectively measure metabolic ratios and apparent diffusion coefficient (ADC) values from 3-Tesla MR spectroscopic imaging (MRSI) and diffusion-weighted imaging (DWI) in benign and malignant peripheral zone (PZ) prostate tissue, assess the parameters' associations with malignancy, and develop and test rules for classifying benign and malignant PZ tissue using whole-mount step-section pathology as the reference standard. METHODS This HIPAA-compliant, IRB-approved study included 67 men (median age, 61 years; range, 41-74 years) with biopsy-proven prostate cancer who underwent preoperative 3 T endorectal multiparametric MRI and had ≥1 PZ lesion >0.1 cm3 at whole-mount histopathology. In benign and malignant PZ regions identified from pathology, voxel-based choline/citrate, polyamines/choline, polyamines/creatine, and (choline + polyamines + creatine)/citrate ratios were averaged, as were ADC values. Patients were randomly split into training and test sets; rules for separating benign from malignant regions were generated with classification and regression tree (CART) analysis and assessed on the test set for sensitivity and specificity. Odds ratios (OR) were evaluated using generalized estimating equations. RESULTS CART analysis of all parameters identified only ADC and (choline + polyamines + creatine)/citrate as significant predictors of cancer. Sensitivity and specificity, respectively, were 0.81 and 0.82 with MRSI-derived, 0.98 and 0.51 with DWI-derived, and 0.79 and 0.90 with MRSI + DWI-derived classification rules. Areas under the curves (AUC) in the test set were 0.93 (0.87-0.97) with ADC, 0.82 (0.72-0.91) with MRSI, and 0.96 (0.92-0.99) with MRSI + ADC. CONCLUSION We developed statistically-based rules for identifying PZ cancer using 3-Tesla MRSI, DWI, and MRSI + DWI and demonstrated the potential value of MRSI + DWI.
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Affiliation(s)
- Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Debra A Goldman
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Toshikazu Takeda
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Victor E Reuter
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Kumar V, Bora GS, Kumar R, Jagannathan NR. Multiparametric (mp) MRI of prostate cancer. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 105:23-40. [PMID: 29548365 DOI: 10.1016/j.pnmrs.2018.01.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2017] [Revised: 01/17/2018] [Accepted: 01/28/2018] [Indexed: 06/08/2023]
Abstract
Prostate cancer (PCa) is one of the most prevalent cancers in men. A large number of men are detected with PCa; however, the clinical behavior ranges from low-grade indolent tumors that never develop into a clinically significant disease to aggressive, invasive tumors that may rapidly progress to metastatic disease. The challenges in clinical management of PCa are at levels of screening, diagnosis, treatment, and follow-up after treatment. Magnetic resonance imaging (MRI) methods have shown a potential role in detection, localization, staging, assessment of aggressiveness, targeting biopsies, etc. in PCa patients. Multiparametric MRI (mpMRI) is emerging as a better option compared to the individual imaging methods used in the evaluation of PCa. There are attempts to improve the reproducibility and reliability of mpMRI by using an objective scoring system proposed in the prostate imaging reporting and data system (PIRADS) for standardized reporting. Prebiopsy mpMRI may be used to detect PCa in men with elevated prostate-specific antigen or abnormal digital rectal examination and to enable targeted biopsies. mpMRI can also be used to decide on clinical management of patients, for example active surveillance, and may help in detecting only the pathology that requires detection. It can potentially not only guide patient selection for initial and repeat biopsy but also reduce false-negative biopsies. This review presents a description of the MR methods most commonly applied for investigations of prostate. The anatomical, functional and metabolic parameters obtained from these MR methods are discussed with regard to their physical basis and their contribution to mpMRI investigations of PCa.
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Affiliation(s)
- Virendra Kumar
- Department of NMR & MRI Facility, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.
| | - Girdhar S Bora
- Department of Urology, Post-Graduate Institute of Medical Sciences, Chandigarh 160012, India
| | - Rajeev Kumar
- Department of Urology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
| | - Naranamangalam R Jagannathan
- Department of NMR & MRI Facility, All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India.
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Sohail A, Bég OA, Li Z, Celik S. Physics of fractional imaging in biomedicine. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2018; 140:13-20. [PMID: 29544821 DOI: 10.1016/j.pbiomolbio.2018.03.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 03/06/2018] [Accepted: 03/08/2018] [Indexed: 12/24/2022]
Abstract
The mathematics of imaging is a growing field of research and is evolving rapidly parallel to evolution in the field of imaging. Imaging, which is a sub-field of biomedical engineering, considers novel approaches to visualize biological tissues with the general goal of improving health. "Medical imaging research provides improved diagnostic tools in clinical settings and supports the development of drugs and other therapies. The data acquisition and diagnostic interpretation with minimum error are the important technical aspects of medical imaging. The image quality and resolution are really important in portraying the internal aspects of patient's body. Although there are several user friendly resources for processing image features, such as enhancement, colour manipulation and compression, the development of new processing methods is still worthy of efforts. In this article we aim to present the role of fractional calculus in imaging with the aid of practical examples.
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Affiliation(s)
- Ayesha Sohail
- Department of Mathematics, Comsats Institute of Information Technology, Lahore, 54000, Pakistan.
| | - O A Bég
- Fluid Mechanics, Bio-Propulsion and Nanosystems, Aeronautical and Mechanical Engineering Department, University of Salford, Newton Building, UG17, Manchester, M54WT, UK.
| | - Zhiwu Li
- School of Electro-Mechanical Engineering, Xidian University, Xi'an, 710071, China; Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau.
| | - Sebahattin Celik
- Department of General Surgery, Yuzuncu Yil University Faculty of Medicine, Van, 65080, Turkey
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deSouza NM, Winfield JM, Waterton JC, Weller A, Papoutsaki MV, Doran SJ, Collins DJ, Fournier L, Sullivan D, Chenevert T, Jackson A, Boss M, Trattnig S, Liu Y. Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives. Eur Radiol 2018; 28:1118-1131. [PMID: 28956113 PMCID: PMC5811587 DOI: 10.1007/s00330-017-4972-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 05/24/2017] [Accepted: 06/28/2017] [Indexed: 12/18/2022]
Abstract
For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology. KEY POINTS • Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.
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Affiliation(s)
- N. M. deSouza
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - J. M. Winfield
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - J. C. Waterton
- Manchester Academic Health Sciences Institute, University of Manchester, Manchester, UK
| | - A. Weller
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - M.-V. Papoutsaki
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - S. J. Doran
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - D. J. Collins
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Foundation Trust, Downs Road, Surrey, SM2 5PT UK
| | - L. Fournier
- Assistance Publique-Hôpitaux de Paris, Hôpital Européen Georges Pompidou, Radiology Department, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - D. Sullivan
- Duke Comprehensive Cancer Institute, Durham, NC USA
| | - T. Chenevert
- Department of Radiology, University of Michigan Health System, Ann Arbor, MI USA
| | - A. Jackson
- Manchester Academic Health Sciences Institute, University of Manchester, Manchester, UK
| | - M. Boss
- Applied Physics Division, National Institute of Standards and Technology (NIST), Boulder, CO USA
| | - S. Trattnig
- Department of Biomedical Imaging and Image guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Y. Liu
- European Organisation for Research and Treatment of Cancer, Headquarters, Brussels, Belgium
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Mayer P, Dinkic C, Jesenofsky R, Klauss M, Schirmacher P, Dapunt U, Hackert T, Uhle F, Hänsch GM, Gaida MM. Changes in the microarchitecture of the pancreatic cancer stroma are linked to neutrophil-dependent reprogramming of stellate cells and reflected by diffusion-weighted magnetic resonance imaging. Theranostics 2018; 8:13-30. [PMID: 29290790 PMCID: PMC5743457 DOI: 10.7150/thno.21089] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 09/13/2017] [Indexed: 01/06/2023] Open
Abstract
In pancreatic cancer (PDAC) intratumor infiltration of polymorphonuclear neutrophils (PMN) is associated with histologically apparent alterations of the tumor growth pattern. The aim of this study was to examine possible associations between PMN infiltration, tumor microarchitecture, and water diffusivity in diffusion-weighted magnetic resonance imaging (DW-MRI), and to further asses the underlying mechanisms. Methods: DW-MRI was performed in 33 PDAC patients prior to surgery. In parallel, tissue specimen were examined histologically for growth pattern, azurocidin-positive PMN infiltrates, and the presence of alpha-smooth muscle actin (α-SMA) and metalloproteinase 9 (MMP9)-positive myofibroblastic cells. For confirmation of the histological findings, a tissue microarray of a second cohort of patients (n=109) was prepared and examined similarly. For in vitro studies, the pancreatic stellate cell line RLT was co-cultivated either with isolated PMN, PMN-lysates, or recombinant azurocidin and characterized by Western blot, flow cytometry, and proteome profiler arrays. Results: Tumors with high PMN density showed restricted water diffusion in DW-MRI and histologic apparent alterations of the tumor microarchitecture (microglandular, micropapillary, or overall poorly differentiated growth pattern) as opposed to tumors with scattered PMN. Areas with altered growth pattern lacked α-SMA-positive myofibroblastic cells. Tissue microarrays confirmed a close association of high PMN density with alterations of the tumor microarchitecture and revealed a significant association of high PMN density with poor histologic grade of differentiation (G3). In vitro experiments provided evidence for direct effects of PMN on stellate cells, where a change to a spindle shaped cell morphology in response to PMN and to PMN-derived azurocidin was seen. Azurocidin incorporated into stellate cells, where it associated with F-actin. Down-regulation of α-SMA was seen within hours, as was activation of the p38-cofilin axis, up-regulation of MMP9, and acquisition of intracellular lipid droplets, which together indicate a phenotype switch of the stellate cells. Conclusion: In PDAC, PMN infiltrates are associated with alterations of the tumor microarchitecture. As a causal relationship, we propose a reprogramming of stellate cells by PMN-derived azurocidin towards a phenotype, which affects the microarchitecture of the tumor.
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Godley KC, Syer TJ, Toms AP, Smith TO, Johnson G, Cameron D, Malcolm PN. Accuracy of high b-value diffusion-weighted MRI for prostate cancer detection: a meta-analysis. Acta Radiol 2018; 59:105-113. [PMID: 28376634 DOI: 10.1177/0284185117702181] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Background The diagnostic accuracy of diffusion-weighted imaging (DWI) to detect prostate cancer is well-established. DWI provides visual as well as quantitative means of detecting tumor, the apparent diffusion coefficient (ADC). Recently higher b-values have been used to improve DWI's diagnostic performance. Purpose To determine the diagnostic performance of high b-value DWI at detecting prostate cancer and whether quantifying ADC improves accuracy. Material and Methods A comprehensive literature search of published and unpublished databases was performed. Eligible studies had histopathologically proven prostate cancer, DWI sequences using b-values ≥ 1000 s/mm2, less than ten patients, and data for creating a 2 × 2 table. Study quality was assessed with QUADAS-2 (Quality Assessment of diagnostic Accuracy Studies). Sensitivity and specificity were calculated and tests for statistical heterogeneity and threshold effect performed. Results were plotted on a summary receiver operating characteristic curve (sROC) and the area under the curve (AUC) determined the diagnostic performance of high b-value DWI. Results Ten studies met eligibility criteria with 13 subsets of data available for analysis, including 522 patients. Pooled sensitivity and specificity were 0.59 (95% confidence interval [CI], 0.57-0.61) and 0.92 (95% CI, 0.91-0.92), respectively, and the sROC AUC was 0.92. Subgroup analysis showed a statistically significant ( P = 0.03) improvement in accuracy when using tumor visual assessment rather than ADC. Conclusion High b-value DWI gives good diagnostic performance for prostate cancer detection and visual assessment of tumor diffusion is significantly more accurate than ROI measurements of ADC.
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Affiliation(s)
- Keith Craig Godley
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
| | | | - Andoni Paul Toms
- Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, UK
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Bao J, Wang X, Hu C, Hou J, Dong F, Guo L. Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI. Eur J Radiol Open 2017; 4:123-128. [PMID: 29034282 PMCID: PMC5633348 DOI: 10.1016/j.ejro.2017.08.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 06/19/2017] [Accepted: 08/29/2017] [Indexed: 01/13/2023] Open
Abstract
Objective To differentiate prostate cancer lesions in transition zone by diffusion-weighted-MRI (DW-MRI). Methods Data from a total of 63 patients who underwent preoperative DWI (b of 0–1000 s/mm2) were prospectively collected and processed by a monoexponential (DWI) model and compared with a biexponential (IVIM) model for quantitation of apparent diffusion coefficients (ADCs), perfusion fraction f, diffusivity D and pseudo-diffusivity D*. Histogram analyses were performed by outlining entire-tumor regions of interest (ROIs). These parameters (separately and combined in a logistic regression model) were used to differentiate lesions depending on histopathological analysis of Magnetic Resonance/transrectal Ultrasound (MR/TRUS) fusion-guided biopsy. The diagnostic ability of differentiate the PCa from BHP in TZ was analyzed by ROC regression. Histogram analysis of quantitative parameters and Gleason score were assessed with Spearman correlation. Results Thirty (30 foci) cases of PCa in PZ and 33 (36 foci) cases of BPH were confirmed by pathology. Mean ADC, median ADC, 10th percentile ADC, 90th percentile ADC, kurtosis and skewness of ADC and mean D values, median D and 90th percentile D differed significantly between PCa and BHP in TZ. The highest classification accuracy was achieved by the mean ADC (0.841) and mean D (0.809). A logistic regression model based on mean ADC and mean D led to an AUC of 0.873, however, the difference is not significant. There were 7 Gleason 6 areas, 9 Gleason 7 areas, 8 Gleason 8 areas, 5 Gleason 9 areas and 2 Gleason 10 areas detected from the 31 prostate cancer areas, the mean Gleason value was(7.5 ± 1.2). The mean ADC and mean D had correlation with Gleason score(r = −0.522 and r = −0.407 respectively, P < 0.05). Conclusion The diagnosis efficiency of IVIM parameters was not superior to ADC in the diagnosis of PCa in TZ. Moreover, the combination of mean ADC and mean D did not perform better than the parameters alone significantly; It is feasible to stratify the pathological grade of prostate cancer by mean ADC.
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Key Words
- ADC, apparent diffusion coefficient
- AUC, Area under the curve
- DCE, dynamic contrast-enhanced imaging
- DWI
- DWI, diffusion-weighted imaging
- IVIM
- IVIM, intravoxel incoherent motion
- MR/TRUS
- MR/TRUS, magnetic resonance/transrectal ultrasound
- MRS, magnetic resonance spectroscopy
- PCa, prostate cancer
- PZ, peripheral zone
- Prostate biopsy
- Prostate cancer
- ROI, region of interest
- T1-VIBE, T1-weighted volumetric interpolated breath-hold examination
- T1WI, T1-weighed imaging
- T2WI, T2-weighted imaging
- TZ, transition zone
- Transition zone
- mpMRI, multiparametric magnetic resonance imaging
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Affiliation(s)
- Jie Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, 188#, Shizi Road, Suzhou, 215006, China
| | - Jianquan Hou
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
| | - Lingchuan Guo
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, 215000, China
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Hejduk B, Bobek-Billewicz B, Rutkowski T, Hebda A, Zawadzka A, Jurkowski MK. Application of Intravoxel Incoherent Motion (IVIM) Model for Differentiation Between Metastatic and Non-Metastatic Head and Neck Lymph Nodes. Pol J Radiol 2017; 82:506-510. [PMID: 29662580 PMCID: PMC5894025 DOI: 10.12659/pjr.902275] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2016] [Accepted: 12/11/2016] [Indexed: 11/18/2022] Open
Abstract
Background Application of intravoxel incoherent motion (IVIM) model parameters, including: true diffusion (D), pseudodiffusion (D*), and perfusion fraction (Fp), for differentiation between metastatic and non-metastatic head and neck lymph nodes. Material/Methods Diffusion-weighted images/apparent diffusion coefficient (DWI/ADC) images of 86 lymph nodes from 31 cancer patients were analyzed. DWI images were obtained with a 1.5T MRI scanner (Magnetom Avanto); b=0,50, 150, 300, 500, 750, 1000, 1200 s/mm2. Results In the study group, there were 32 (37%) and 54 (67%) metastatic and non-metastatic lymph nodes, respectively. The mean values of D, D*, and Fp did not differ significantly between metastatic and non-metastatic lymph nodes. Conclusions IVIM parameters are not useful for differentiation between metastatic and non-metastatic head and neck lymph nodes.
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Affiliation(s)
- Beata Hejduk
- Department of Radiology and Diagnostic Imaging, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland
| | - Barbara Bobek-Billewicz
- Department of Radiology and Diagnostic Imaging, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland
| | - Tomasz Rutkowski
- I Clinic of Radiotherapy and Chemotherapy, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland
| | - Anna Hebda
- Department of Radiology and Diagnostic Imaging, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland
| | - Agata Zawadzka
- Department of Radiology and Diagnostic Imaging, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland
| | - Marek K Jurkowski
- Department of Medical Physics, Center of Oncology - M. Skłodowska-Curie Memorial Institute, Branch in Gliwice, Gliwice, Poland.,Department of Medical Analytics, University of Warmia and Mazury, Olsztyn, Poland
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Zhang XY, Li XT, Sun J, Sun YS. Initial experience of correlating diffusion spectral parameters with histopathologic indexes in murine colorectal tumor homografts. Onco Targets Ther 2017; 10:4213-4223. [PMID: 28894378 PMCID: PMC5584890 DOI: 10.2147/ott.s127283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Purpose To determine the correlation between continuously distributed diffusion-weighted image (DWI)-derived parameters and histopathologic indexes. Methods Fifty-four mice bearing HCT-116 colorectal tumors were included for analysis; 12 mice were used for continuous observation, and the other 42 mice were used for break-point observation. All mice were randomly divided into radiotherapy and non-radiotherapy groups. Optical imaging and MRI were performed at different time points according to radiotherapy regimen (baseline, 24 h, 48 h, 72 h, 7 d, 14 d, and 28 d). Continuous observation data were analyzed to show the difference of dynamic changing trends of optical and MR-DWI–derived parameters between radiotherapy and non-radiotherapy groups (photon numbers, D_max, full width half maximum [FWHM], and apparent diffusion coefficient [ADC] value). Break-point observation data were used to analyze the correlation between histopathologic indices and DWI-derived parameters. Results There was a significant difference in the changing trends of photon numbers, D_max, FWHM, and ADC value between radiotherapy and non-radiotherapy groups, especially at early time points. There was moderate negative correlation between Ki67 and percentage changes of D_max, FWHM, and ADC values (the correlation coefficients were 0.632, 0.449, and 0.586, P<0.001, P=0.008, and P<0.001, respectively). There was moderate negative correlation between survivin and percentage changes of D_max and ADC values (correlation coefficients were 0.496 and 0.473, P=0.004 and P=0.006, respectively). Conclusion The continuously distributed DWI-derived parameters could reflect histological behavior to some extent and, thus, are potential markers for early noninvasive monitoring of tumor cell apoptosis and proliferation.
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Affiliation(s)
- Xiao-Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China
| | - Jia Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, People's Republic of China
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Chen WB, Zhang B, Liang L, Dong YH, Cai GH, Liang CH, Lan BW, Zhang SX. To predict the radiosensitivity of nasopharyngeal carcinoma using intravoxel incoherent motion MRI at 3.0 T. Oncotarget 2017; 8:53740-53750. [PMID: 28881847 PMCID: PMC5581146 DOI: 10.18632/oncotarget.17367] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 04/11/2017] [Indexed: 02/05/2023] Open
Abstract
PURPOSE To investigate intravoxel incoherent motion (IVIM) MRI for evaluating the sensitivity of radiotherapy on nasopharyngeal carcinoma (NPC). RESULTS The reproducibility between intra-observer and inter-observer was relatively good. D (0.72×10-3 mm2/s±0.14 vs. 0.54×10-3 mm2/s±0.23; P < 0.001) and D* (157.92×10-3 mm2/s±15.21 vs. 120.36×10-3 mm2/s±10.22; P < 0.0001) were significantly higher in effective group than poor-effective group, whereas the difference of f (18.79%±2.51 vs. 16.47%±1.51) and ADC (1.21×10-3 mm2/s±0.11 vs. 1.33×10-3 mm2/s±0.23) could not reach statistical significant between the 2 groups (P > 0.05). CONCLUSIONS IVIM may be potentially useful in assessing the radiosensitivity of NPC. The higher D value combining with higher D* value might indicate the more radiosensitive of NPC, and increased D* might reflect increased blood vessel generation and parenchymal perfusion in NPC. MATERIALS AND METHODS Sixty consecutive patients (20 female, range, 27-83 years, mean age, 52 years) newly diagnosed NPC in the stage of T3 or T4 were enrolled. Forty-two of them were divided into effective group clinically after a standard radiotherapy according to the RECIST criteria. IVIM with 13 b-values (range, 0-800 s/mm2) and general MRI were performed at 3.0T MR scanner before and after radiotherapy. The parameters of IVIM including perfusion fraction (f), perfusion-related diffusion (D*), pure molecular diffusion (D) and apparent diffusion coefficient (ADC) were calculated. Two radiologists major in MRI diagnose analyzed all images independently and placed regions of interest (ROIs). Intra-class correlation coefficient (ICC) was used to evaluate intra-observer and inter-observer agreement. And Mann-Whitney test was used to assess the differences between the two groups.
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Affiliation(s)
- Wen Bo Chen
- Department of Radiology, HuiZhou Municipal Central Hospital, Huizhou, Guangdong, P.R. China
| | - Bin Zhang
- Department of Radiology, Guangdong Academy of Medical Sciences/Guangdong General Hospital, Guangzhou, Guangdong, P.R. China
- Southern Medical University, Guangzhou, Guangdong, P.R. China
| | - Long Liang
- Department of Radiology, Guangdong Academy of Medical Sciences/Guangdong General Hospital, Guangzhou, Guangdong, P.R. China
| | - Yu Hao Dong
- Department of Radiology, Guangdong Academy of Medical Sciences/Guangdong General Hospital, Guangzhou, Guangdong, P.R. China
- Shantou University Medical College, Shantou, Guangdong, P.R. China
| | - Guan Hui Cai
- Department of Radiology, HuiZhou Municipal Central Hospital, Huizhou, Guangdong, P.R. China
| | - Chang Hong Liang
- Department of Radiology, Guangdong Academy of Medical Sciences/Guangdong General Hospital, Guangzhou, Guangdong, P.R. China
| | - Bo Wen Lan
- Department of Radiology, HuiZhou Municipal Central Hospital, Huizhou, Guangdong, P.R. China
| | - Shui Xing Zhang
- Department of Radiology, Guangdong Academy of Medical Sciences/Guangdong General Hospital, Guangzhou, Guangdong, P.R. China
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Reischauer C, Patzwahl R, Koh DM, Froehlich JM, Gutzeit A. Non-Mono-Exponential Analysis of Diffusion-Weighted Imaging for Treatment Monitoring in Prostate Cancer Bone Metastases. Sci Rep 2017; 7:5809. [PMID: 28724944 PMCID: PMC5517576 DOI: 10.1038/s41598-017-06246-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2017] [Accepted: 06/27/2017] [Indexed: 01/14/2023] Open
Abstract
Diffusion-weighted imaging quantified using the mono-exponential model has shown great promise for monitoring treatment response in prostate cancer bone metastases. The aim of this prospective study is to evaluate whether non-mono-exponential diffusion models better describe the water diffusion properties and may improve treatment response assessment. Diffusion-weighted imaging data of 12 treatment-naïve patients with 34 metastases acquired before and at one, two, and three months after initiation of antiandrogen treatment are analysed using the mono-exponential, the intravoxel incoherent motion, the stretched exponential, and the statistical model. Repeatability of the fitted parameters and changes under therapy are quantified. Model preference is assessed and correlation coefficients across times are calculated to delineate the relationship between the prostate-specific antigen levels and the diffusion parameters as well as between the diffusion parameters within each model. There is a clear preference for non-mono-exponential diffusion models at all time points. Particularly the stretched exponential is favoured in approximately 60% of the lesions. Its parameters increase significantly in response to treatment and are highly repeatable. Thus, the stretched exponential may be utilized as a potential optimal model for monitoring treatment response. Compared with the mono-exponential model, it may provide complementary information on tissue properties and improve response assessment.
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Affiliation(s)
- Carolin Reischauer
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland.
- Institute for Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland.
| | - René Patzwahl
- Department of Radiology, Cantonal Hospital Winterthur, Winterthur, Switzerland
| | - Dow-Mu Koh
- Academic Department of Radiology, Royal Marsden NHS Foundation Trust, Sutton, Surrey, UK
- CR-UK and EPSRC Cancer Imaging Centre, Institute of Cancer Research, Sutton, Surrey, UK
| | - Johannes M Froehlich
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
| | - Andreas Gutzeit
- Institute of Radiology and Nuclear Medicine, Clinical Research Unit, Hirslanden Hospital St. Anna, Lucerne, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
- Department of Radiology, Paracelsus Medical University Salzburg, Salzburg, Austria
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Langkilde F, Kobus T, Fedorov A, Dunne R, Tempany C, Mulkern RV, Maier SE. Evaluation of fitting models for prostate tissue characterization using extended-range b-factor diffusion-weighted imaging. Magn Reson Med 2017; 79:2346-2358. [PMID: 28718517 DOI: 10.1002/mrm.26831] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 06/16/2017] [Accepted: 06/19/2017] [Indexed: 11/08/2022]
Abstract
PURPOSE To compare the fitting and tissue discrimination performance of biexponential, kurtosis, stretched exponential, and gamma distribution models for high b-factor diffusion-weighted images in prostate cancer. METHODS Diffusion-weighted images with 15 b-factors ranging from b = 0 to 3500 s/mm2 were obtained in 62 prostate cancer patients. Pixel-wise signal decay fits for each model were evaluated with the Akaike Information Criterion (AIC). Parameter values for each model were determined within normal prostate and the index lesion. Their potential to differentiate normal from cancerous tissue was investigated through receiver operating characteristic analysis and comparison with Gleason score. RESULTS The biexponential slow diffusion fraction fslow , the apparent kurtosis diffusion coefficient ADCK , and the excess kurtosis factor K differ significantly among normal peripheral zone (PZ), normal transition zone (TZ), tumor PZ, and tumor TZ. Biexponential and gamma distribution models result in the lowest AIC, indicating a superior fit. Maximum areas under the curve (AUCs) of all models ranged from 0.93 to 0.96 for the PZ and from 0.95 to 0.97 for the TZ. Similar AUCs also result from the apparent diffusion coefficient (ADC) of a monoexponential fit to a b-factor sub-range up to 1250 s/mm2 . For kurtosis and stretched exponential models, single parameters yield the highest AUCs, whereas for the biexponential and gamma distribution models, linear combinations of parameters produce the highest AUCs. Parameters with high AUC show a trend in differentiating low from high Gleason score, whereas parameters with low AUC show no such ability. CONCLUSION All models, including a monoexponential fit to a lower-b sub-range, achieve similar AUCs for discrimination of normal and cancer tissue. The biexponential model, which is favored statistically, also appears to provide insight into disease-related microstructural changes. Magn Reson Med 79:2346-2358, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Fredrik Langkilde
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - Thiele Kobus
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology and Nuclear Medicine, Radboud university medical center, Nijmegen, The Netherlands
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ruth Dunne
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Clare Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert V Mulkern
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan E Maier
- Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Winfield JM, Orton MR, Collins DJ, Ind TEJ, Attygalle A, Hazell S, Morgan VA, deSouza NM. Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. Eur Radiol 2017; 27:627-636. [PMID: 27221560 PMCID: PMC5209433 DOI: 10.1007/s00330-016-4417-0] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2016] [Revised: 04/15/2016] [Accepted: 05/13/2016] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Assessment of empirical diffusion-weighted MRI (DW-MRI) models in cervical tumours to investigate whether fitted parameters distinguish between types and grades of tumours. METHODS Forty-two patients (24 squamous cell carcinomas, 14 well/moderately differentiated, 10 poorly differentiated; 15 adenocarcinomas, 13 well/moderately differentiated, two poorly differentiated; three rare types) were imaged at 3 T using nine b-values (0 to 800 s mm-2). Mono-exponential, stretched exponential, kurtosis, statistical, and bi-exponential models were fitted. Model preference was assessed using Bayesian Information Criterion analysis. Differences in fitted parameters between tumour types/grades and correlation between fitted parameters were assessed using two-way analysis of variance and Pearson's linear correlation coefficient, respectively. RESULTS Non-mono-exponential models were preferred by 83 % of tumours with bi-exponential and stretched exponential models preferred by the largest numbers of tumours. Apparent diffusion coefficient (ADC) and diffusion coefficients from non-mono-exponential models were significantly lower in poorly differentiated tumours than well/moderately differentiated tumours. α (stretched exponential), K (kurtosis), f and D* (bi-exponential) were significantly different between tumour types. Strong correlation was observed between ADC and diffusion coefficients from other models. CONCLUSIONS Non-mono-exponential models were preferred to the mono-exponential model in DW-MRI data from cervical tumours. Parameters of non-mono-exponential models showed significant differences between types and grades of tumours. KEY POINTS • Non-mono-exponential DW-MRI models are preferred in the majority of cervical tumours. • Poorly differentiated cervical tumours exhibit lower diffusion coefficients than well/moderately differentiated tumours. • Non-mono-exponential model parameters α, K, f, and D* differ between tumour types. • Micro-structural features are likely to affect parameters in non-mono-exponential models differently.
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Affiliation(s)
- Jessica M Winfield
- MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK.
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK.
| | - Matthew R Orton
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - David J Collins
- MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Thomas E J Ind
- Gynaecology Unit, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Ayoma Attygalle
- Department of Histopathology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Steve Hazell
- Department of Histopathology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK
| | - Veronica A Morgan
- MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
| | - Nandita M deSouza
- MRI Unit, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
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Pesapane F, Patella F, Fumarola EM, Panella S, Ierardi AM, Pompili GG, Franceschelli G, Angileri SA, Magenta Biasina A, Carrafiello G. Intravoxel Incoherent Motion (IVIM) Diffusion Weighted Imaging (DWI) in the Periferic Prostate Cancer Detection and Stratification. Med Oncol 2017; 34:35. [PMID: 28144814 DOI: 10.1007/s12032-017-0892-7] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Accepted: 01/16/2017] [Indexed: 12/18/2022]
Abstract
The aim of this study was to compare the Intravoxel Incoherent Motion (IVIM) parameters between healthy Peripheral Zone (PZ), Benign Prostatic Hyperplasia (BPH) and Prostate Cancer (PCa) and compare them to assess whether there was correlation with Gleason Score (GS) grading system. Thirty-one patients with suspect of PCa underwent 1.5T Multi-Parametric Magnetic Resonance Imaging (MP-MRI) with endorectal coil with a protocol including T2WI, DWI using 10 b values (0, 10, 20, 30, 50, 80, 100, 200, 400, 1000 s/mm2) and DCE. Monoexponential and IVIM model fits were used to calculate both apparent diffusion coefficient (ADC) and the following IVIM parameters: molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*) and perfusion fraction (f). The ADC and D values were significantly lower in the PCa (0.70 ± 0.16 × 10-3 mm2/s and 0.88 ± 0.31 × 10-3 mm2/s) compared to those found in the PZ (1.22 ± 0.20 × 10-3 mm2/s and 1.78 ± 0.34 × 10-3 mm2/s) and in the BPH (1.53 ± 0.23 × 10-3 mm2/s and 1.11 ± 0.28 × 10-3 mm2/s). The D* parameter was significantly increased in the PCa (5.35 ± 5.12 × 10-3 mm2/s) compare to the healthy PZ (3.02 ± 2.86 × 10-3 mm2/s), instead there was not significantly difference in the PCa compare to the BPH (5.61 ± 6.77 × 10-3 mm2/s). The f was statistically lower in the PCa (9.01 ± 5.20%) compared to PZ (10.57 ± 9.30%), but not significantly different between PCa and BPH (9.29 ± 7.29%). The specificity, sensitivity and accuracy of T2WI associated with DWI and IVIM were higher (100, 98 and 99%, respectively) than for T2WI/DWI and IVIM alone (89, 92 and 90%, respectively). Only for ADC was found a statistical difference between low- and intermediate-/high-grade tumors. Adding IVIM to the MP-MRI could increase the diagnostic performance to detect clinically relevant PCa. ADC values have been found to have a rule to discriminate PCa reliably from normal areas and differed significantly in low- and intermediate-/high-grade PCa. In contrast, IVIM parameters were unable to distinguish between the different GS.
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Affiliation(s)
- Filippo Pesapane
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy.
| | - Francesca Patella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Enrico Maria Fumarola
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Silvia Panella
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Anna Maria Ierardi
- Department of Radiology, Interventional Radiology, Insubria University, Varese, Italy
| | - Giovanni Guido Pompili
- Department of Health Sciences, Diagnostic and Interventional Radiology, San Paolo Hospital, Università degli Studi di Milano, Via A di Rudinì 8, 20142, Milan, Italy
| | - Giuseppe Franceschelli
- Department of Health Sciences, Diagnostic and Interventional Radiology, San Paolo Hospital, Università degli Studi di Milano, Via A di Rudinì 8, 20142, Milan, Italy
| | - Salvatore Alessio Angileri
- Department of Health Sciences, Diagnostic and Interventional Radiology, San Paolo Hospital, Università degli Studi di Milano, Via A di Rudinì 8, 20142, Milan, Italy
| | - Alberto Magenta Biasina
- Department of Health Sciences, Diagnostic and Interventional Radiology, San Paolo Hospital, Università degli Studi di Milano, Via A di Rudinì 8, 20142, Milan, Italy
| | - Gianpaolo Carrafiello
- Department of Health Sciences, Diagnostic and Interventional Radiology, San Paolo Hospital, Università degli Studi di Milano, Via A di Rudinì 8, 20142, Milan, Italy
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Manias KA, Gill SK, MacPherson L, Foster K, Oates A, Peet AC. Magnetic resonance imaging based functional imaging in paediatric oncology. Eur J Cancer 2016; 72:251-265. [PMID: 28011138 DOI: 10.1016/j.ejca.2016.10.037] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/26/2016] [Accepted: 10/30/2016] [Indexed: 12/16/2022]
Abstract
Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.
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Affiliation(s)
- Karen A Manias
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; Department of Paediatric Oncology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
| | - Simrandip K Gill
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; Department of Paediatric Oncology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
| | - Lesley MacPherson
- Department of Radiology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
| | - Katharine Foster
- Department of Radiology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
| | - Adam Oates
- Department of Radiology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
| | - Andrew C Peet
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK; Department of Paediatric Oncology, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, B4 6NH, UK.
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Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI. Sci Rep 2016; 6:38782. [PMID: 27934928 PMCID: PMC5146921 DOI: 10.1038/srep38782] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Accepted: 11/14/2016] [Indexed: 02/07/2023] Open
Abstract
Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R2 = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.
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Johnston E, Pye H, Bonet-Carne E, Panagiotaki E, Patel D, Galazi M, Heavey S, Carmona L, Freeman A, Trevisan G, Allen C, Kirkham A, Burling K, Stevens N, Hawkes D, Emberton M, Moore C, Ahmed HU, Atkinson D, Rodriguez-Justo M, Ng T, Alexander D, Whitaker H, Punwani S. INNOVATE: A prospective cohort study combining serum and urinary biomarkers with novel diffusion-weighted magnetic resonance imaging for the prediction and characterization of prostate cancer. BMC Cancer 2016; 16:816. [PMID: 27769214 PMCID: PMC5073433 DOI: 10.1186/s12885-016-2856-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 10/12/2016] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Whilst multi-parametric magnetic resonance imaging (mp-MRI) has been a significant advance in the diagnosis of prostate cancer, scanning all patients with elevated prostate specific antigen (PSA) levels is considered too costly for widespread National Health Service (NHS) use, as the predictive value of PSA levels for significant disease is poor. Despite the fact that novel blood and urine tests are available which may predict aggressive disease better than PSA, they are not routinely employed due to a lack of clinical validity studies. Furthermore approximately 40 % of mp-MRI studies are reported as indeterminate, which can lead to repeat examinations or unnecessary biopsy with associated patient anxiety, discomfort, risk and additional costs. METHODS/DESIGN We aim to clinically validate a panel of minimally invasive promising blood and urine biomarkers, to better select patients that will benefit from a multiparametric prostate MRI. We will then test whether the performance of the mp-MRI can be improved by the addition of an advanced diffusion-weighted MRI technique, which uses a biophysical model to characterise tissue microstructure called VERDICT; Vascular and Extracellular Restricted Diffusion for Cytometry in Tumours. INNOVATE is a prospective single centre cohort study in 365 patients. mp-MRI will act as the reference standard for the biomarker panel. A clinical outcome based reference standard based on biopsy, mp-MRI and follow-up will be used for VERDICT MRI. DISCUSSION We expect the combined effect of biomarkers and VERDICT MRI will improve care by better detecting aggressive prostate cancer early and make mp-MRI before biopsy economically viable for universal NHS adoption. TRIAL REGISTRATION INNOVATE is registered on ClinicalTrials.gov, with reference NCT02689271 .
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Affiliation(s)
- Edward Johnston
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
| | - Hayley Pye
- Research Department for Tissue & Energy, Division of Surgery & Interventional Science, Wing 2.4 Cruciform Building, Gower Street, London, WC1E 6BT UK
- Addenbrookes Hospital, Level 4, Pathology Block, Hills Road, Cambridge, CB20QQ UK
| | | | | | - Dominic Patel
- Department of Research Pathology, UCL Cancer Institute, Rockefeller Building, 21 University Street, London, WC1E 6JJ UK
| | - Myria Galazi
- Molecular Oncology group, UCL Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London, WC1E 6DD UK
| | - Susan Heavey
- Research Department for Tissue & Energy, Division of Surgery & Interventional Science, Wing 2.4 Cruciform Building, Gower Street, London, WC1E 6BT UK
- Addenbrookes Hospital, Level 4, Pathology Block, Hills Road, Cambridge, CB20QQ UK
| | - Lina Carmona
- Research Department for Tissue & Energy, Division of Surgery & Interventional Science, Wing 2.4 Cruciform Building, Gower Street, London, WC1E 6BT UK
- Addenbrookes Hospital, Level 4, Pathology Block, Hills Road, Cambridge, CB20QQ UK
| | - Alexander Freeman
- Department of Research Pathology, UCL Cancer Institute, Rockefeller Building, 21 University Street, London, WC1E 6JJ UK
| | - Giorgia Trevisan
- Department of Research Pathology, UCL Cancer Institute, Rockefeller Building, 21 University Street, London, WC1E 6JJ UK
| | - Clare Allen
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
| | - Alexander Kirkham
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
| | - Keith Burling
- Research Department for Tissue & Energy, Division of Surgery & Interventional Science, Wing 2.4 Cruciform Building, Gower Street, London, WC1E 6BT UK
- Addenbrookes Hospital, Level 4, Pathology Block, Hills Road, Cambridge, CB20QQ UK
| | - Nicola Stevens
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
| | - David Hawkes
- Department of Computer Science, UCL, Gower Street, London, WC1E 6BT UK
| | - Mark Emberton
- Division of Surgery, 4th floor, 21 University Street, London, WC1E UK
| | - Caroline Moore
- Division of Surgery, 4th floor, 21 University Street, London, WC1E UK
| | - Hashim U Ahmed
- Division of Surgery, 4th floor, 21 University Street, London, WC1E UK
| | - David Atkinson
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
| | - Manuel Rodriguez-Justo
- Department of Research Pathology, UCL Cancer Institute, Rockefeller Building, 21 University Street, London, WC1E 6JJ UK
| | - Tony Ng
- Molecular Oncology group, UCL Cancer Institute, Paul O’Gorman Building, 72 Huntley Street, London, WC1E 6DD UK
| | - Daniel Alexander
- Department of Computer Science, UCL, Gower Street, London, WC1E 6BT UK
| | - Hayley Whitaker
- Research Department for Tissue & Energy, Division of Surgery & Interventional Science, Wing 2.4 Cruciform Building, Gower Street, London, WC1E 6BT UK
- Addenbrookes Hospital, Level 4, Pathology Block, Hills Road, Cambridge, CB20QQ UK
| | - Shonit Punwani
- UCL Centre for Medical Imaging, 5th floor, Wolfson House, 4 Stephenson Way, London, NW1 2HE UK
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Guo T, Chen J, Wu B, Zheng D, Jiao S, Song Y, Chen M. Use of intravoxel incoherent motion diffusion-weighted imaging in identifying the vascular and avascular zones of human meniscus. J Magn Reson Imaging 2016; 45:1090-1096. [PMID: 27661458 DOI: 10.1002/jmri.25476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2016] [Accepted: 08/29/2016] [Indexed: 01/10/2023] Open
Affiliation(s)
- Tan Guo
- Department of Radiology; Beijing Hospital; Beijing China
| | - Juan Chen
- Department of Radiology; Beijing Hospital; Beijing China
| | - Bing Wu
- GE Healthcare China; Beijing China
| | | | - Sheng Jiao
- Department of Radiology; Beijing Hospital; Beijing China
| | - Yan Song
- Department of Radiology; Beijing Hospital; Beijing China
| | - Min Chen
- Department of Radiology; Beijing Hospital; Beijing China
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Application of an unsupervised multi-characteristic framework for intermediate-high risk prostate cancer localization using diffusion-weighted MRI. Magn Reson Imaging 2016; 34:1227-1234. [PMID: 27451403 DOI: 10.1016/j.mri.2016.06.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 06/14/2016] [Accepted: 06/26/2016] [Indexed: 12/22/2022]
Abstract
PURPOSE The aim of this proof-of-concept work is to propose an unsupervised framework that combines multiple parameters, in "positive-if-all-positive" manner, from different models to localize tumors. METHODS A voxel-by-voxel analysis of the DW-MRI images of whole prostate was performed to obtain parametric maps for D*, D, f, and K using the IVIM and kurtosis models. Ten patients with moderate or high-risk prostate cancer were included in study. The mean age and serum PSA for these 10 patients were 65years (range 54-78) and 21.9ng/mL (range 4.84-44.81), respectively. These patients were scanned using a DW spin-echo sequence with echo-planar readout with 16 equidistantly spaced b-values in the range of 0-2000s/mm2 (TE=58ms; TR=3990ms; spatial resolution 2.19×2.19×2.73mm3, slices =26, FOV=140×140mm, slice gap =0.27mm, NSA=2). RESULTS The proposed framework detected 24 lesions of which 14 were true positive with 58% tumor detection rate on lesion-based analysis with sensitivity of 100%. The mpMRI evaluation (PIRADSv2) identified 12 of 14 true positive lesions with sensitivity of 86%; positive predictive value of mpMRI was 92%. The index lesions were visible on all framework maps and were coded as the most suspicious in 9 of 10 patients. CONCLUSION Preliminary results of the proposed framework indicate high patient-based sensitivity with 100% detection rate for identifying moderate-high risk aggressive index lesions.
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Bourne R, Panagiotaki E. Limitations and Prospects for Diffusion-Weighted MRI of the Prostate. Diagnostics (Basel) 2016; 6:E21. [PMID: 27240408 PMCID: PMC4931416 DOI: 10.3390/diagnostics6020021] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Revised: 05/23/2016] [Accepted: 05/23/2016] [Indexed: 12/22/2022] Open
Abstract
Diffusion-weighted imaging (DWI) is the most effective component of the modern multi-parametric magnetic resonance imaging (mpMRI) scan for prostate pathology. DWI provides the strongest prediction of cancer volume, and the apparent diffusion coefficient (ADC) correlates moderately with Gleason grade. Notwithstanding the demonstrated cancer assessment value of DWI, the standard measurement and signal analysis methods are based on a model of water diffusion dynamics that is well known to be invalid in human tissue. This review describes the biophysical limitations of the DWI component of the current standard mpMRI protocol and the potential for significantly improved cancer assessment performance based on more sophisticated measurement and signal modeling techniques.
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Affiliation(s)
- Roger Bourne
- Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, NSW 2006, Australia.
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Iima M, Le Bihan D. Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future. Radiology 2016; 278:13-32. [PMID: 26690990 DOI: 10.1148/radiol.2015150244] [Citation(s) in RCA: 373] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The concept of diffusion magnetic resonance (MR) imaging emerged in the mid-1980s, together with the first images of water diffusion in the human brain, as a way to probe tissue structure at a microscopic scale, although the images were acquired at a millimetric scale. Since then, diffusion MR imaging has become a pillar of modern clinical imaging. Diffusion MR imaging has mainly been used to investigate neurologic disorders. A dramatic application of diffusion MR imaging has been acute brain ischemia, providing patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable, thus avoiding terrible handicaps. On the other hand, it was found that water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the nerve fibers. This feature can be exploited to produce stunning maps of the orientation in space of the white matter tracts and brain connections in just a few minutes. Diffusion MR imaging is now also rapidly expanding in oncology, for the detection of malignant lesions and metastases, as well as monitoring. Water diffusion is usually largely decreased in malignant tissues, and body diffusion MR imaging, which does not require any tracer injection, is rapidly becoming a modality of choice to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. After a brief summary of the key methodological concepts beyond diffusion MR imaging, this article will give a review of the clinical literature, mainly focusing on current outstanding issues, followed by some innovative proposals for future improvements.
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Affiliation(s)
- Mami Iima
- From the Department of Diagnostic Imaging and Nuclear Medicine (M.I.) and the Human Brain Research Center (D.L.B.), Kyoto University Graduate School of Medicine, and the Hakubi Center for Advanced Research (M.I.), Kyoto University, Kyoto, Japan; and NeuroSpin, CEA/DSV/I2BM, Bât 145, Point Courrier 156, CEA-Saclay Center, F-91191 Gif-sur-Yvette, France (D.L.B.)
| | - Denis Le Bihan
- From the Department of Diagnostic Imaging and Nuclear Medicine (M.I.) and the Human Brain Research Center (D.L.B.), Kyoto University Graduate School of Medicine, and the Hakubi Center for Advanced Research (M.I.), Kyoto University, Kyoto, Japan; and NeuroSpin, CEA/DSV/I2BM, Bât 145, Point Courrier 156, CEA-Saclay Center, F-91191 Gif-sur-Yvette, France (D.L.B.)
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Merisaari H, Movahedi P, Perez IM, Toivonen J, Pesola M, Taimen P, Boström PJ, Pahikkala T, Kiviniemi A, Aronen HJ, Jambor I. Fitting methods for intravoxel incoherent motion imaging of prostate cancer on region of interest level: Repeatability and gleason score prediction. Magn Reson Med 2016; 77:1249-1264. [PMID: 26924552 DOI: 10.1002/mrm.26169] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 01/25/2016] [Accepted: 01/26/2016] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate different fitting methods for intravoxel incoherent motion (IVIM) imaging of prostate cancer in the terms of repeatability and Gleason score prediction. METHODS Eighty-one patients with histologically confirmed prostate cancer underwent two repeated 3 Tesla diffusion-weighted imaging (DWI) examinations performed using 14 b-values in the range of 0-500 s/mm2 and diffusion time of 19.004 ms. Mean signal intensities of regions-of-interest were fitted using five different fitting methods for IVIM as well as monoexponential, kurtosis, and stretched exponential models. The fitting methods and models were evaluated in the terms of fitting quality [Akaike information criteria (AIC)], repeatability, and Gleason score prediction. Tumors were classified into three groups (3 + 3, 3 + 4, > 3 + 4). Machine learning algorithms were used to evaluate the performance of the combined use of the parameters. Simulation studies were performed to evaluate robustness of the fitting methods against noise. RESULTS Monoexponential model was preferred over IVIM based on AIC. The "pseudodiffusion" parameters demonstrated low repeatability and clinical value. Median "pseudodiffusion" fraction values were below 8.00%. Combined use of the parameters did not outperform the monoexponential model. CONCLUSION Monoexponential model demonstrated the highest repeatability and clinical values in the regions-of-interest based analysis of prostate cancer DWI, b-values in the range of 0-500 s/mm2 . Magn Reson Med 77:1249-1264, 2017. © 2016 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Parisa Movahedi
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Ileana M Perez
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Jussi Toivonen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Department of Information Technology, University of Turku, Turku, Finland
| | - Aida Kiviniemi
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland.,Department of Information Technology, University of Turku, Turku, Finland
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Jafar MM, Parsai A, Miquel ME. Diffusion-weighted magnetic resonance imaging in cancer: Reported apparent diffusion coefficients, in-vitro and in-vivo reproducibility. World J Radiol 2016; 8:21-49. [PMID: 26834942 PMCID: PMC4731347 DOI: 10.4329/wjr.v8.i1.21] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Revised: 11/10/2015] [Accepted: 12/07/2015] [Indexed: 02/06/2023] Open
Abstract
There is considerable disparity in the published apparent diffusion coefficient (ADC) values across different anatomies. Institutions are increasingly assessing repeatability and reproducibility of the derived ADC to determine its variation, which could potentially be used as an indicator in determining tumour aggressiveness or assessing tumour response. In this manuscript, a review of selected articles published to date in healthy extra-cranial body diffusion-weighted magnetic resonance imaging is presented, detailing reported ADC values and discussing their variation across different studies. In total 115 studies were selected including 28 for liver parenchyma, 15 for kidney (renal parenchyma), 14 for spleen, 13 for pancreatic body, 6 for gallbladder, 13 for prostate, 13 for uterus (endometrium, myometrium, cervix) and 13 for fibroglandular breast tissue. Median ADC values in selected studies were found to be 1.28 × 10(-3) mm(2)/s in liver, 1.94 × 10(-3) mm(2)/s in kidney, 1.60 × 10(-3) mm(2)/s in pancreatic body, 0.85 × 10(-3) mm(2)/s in spleen, 2.73 × 10(-3) mm(2)/s in gallbladder, 1.64 × 10(-3) mm(2)/s and 1.31 × 10(-3) mm(2)/s in prostate peripheral zone and central gland respectively (combined median value of 1.54×10(-3) mm(2)/s), 1.44 × 10(-3) mm(2)/s in endometrium, 1.53 × 10(-3) mm(2)/s in myometrium, 1.71 × 10(-3) mm(2)/s in cervix and 1.92 × 10(-3) mm(2)/s in breast. In addition, six phantom studies and thirteen in vivo studies were summarized to compare repeatability and reproducibility of the measured ADC. All selected phantom studies demonstrated lower intra-scanner and inter-scanner variation compared to in vivo studies. Based on the findings of this manuscript, it is recommended that protocols need to be optimised for the body part studied and that system-induced variability must be established using a standardized phantom in any clinical study. Reproducibility of the measured ADC must also be assessed in a volunteer population, as variations are far more significant in vivo compared with phantom studies.
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Valerio M, Zini C, Fierro D, Giura F, Colarieti A, Giuliani A, Laghi A, Catalano C, Panebianco V. 3T multiparametric MRI of the prostate: Does intravoxel incoherent motion diffusion imaging have a role in the detection and stratification of prostate cancer in the peripheral zone? Eur J Radiol 2016; 85:790-4. [PMID: 26971425 DOI: 10.1016/j.ejrad.2016.01.006] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 01/13/2016] [Accepted: 01/16/2016] [Indexed: 11/30/2022]
Abstract
PURPOSE To evaluate the potential added value of the intravoxel incoherent motion model to conventional multiparametric magnetic resonance protocol in order to differentiate between healthy and neoplastic prostate tissue in the peripheral zone. MATERIAL AND METHODS Mono-exponential and bi-exponential fits were used to calculate ADC and IVIM parameters in 53 patients with peripheral zone biopsy proved tumor. Inferential statistics analysis was performed on T2, ADC and IVIM parameters (D, D*, f) comparing healthy and neoplastic tissues. Linear discriminant analysis was performed for the conventional parameters (T2 and ADC), the IVIM parameters (molecular diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f) and the combined T2-weighted imaging/DWI and IVIM parameters (T2, ADC, D, D* and f). A correlation with Gleason scores was achieved. RESULTS The values of T2, ADC and D were significantly lower in cancerous tissues (2749.82 ± 1324.67 ms, 0.76 ± 0.27 × 10(-3)mm(2)/s and 0.99 ± 0.38 × 10(-3)mm(2)/s respectively) compared to those found in the healthy tissues (3750.70 ± 1735.37 ms, 1.39 ± 0.48 × 10(-3)mm(2)/s and 1.77 ± 0.36 × 10(-3)mm(2)/s respectively); D* parameter was significantly increased in neoplastic compared to healthy tissue (15.56 ± 12.91 × 10(-3)mm(2)/s and 10.25 ± 10.52 × 10(-3)mm(2)/s respectively). The specificity, sensitivity and accuracy of the T2-weighted imaging/DWI and IVIM parameters were 100, 96 and 98%, respectively, compare to 88, 92 and 90% and 96, 92 and 94 for T2-weighted imaging/ADC and IVIM alone. CONCLUSIONS IVIM parameters increase the specificity and sensitivity in the evaluation of peripheral zone prostate cancer. A statistical difference between low grade tumors and high grade tumors has been demostrated in that ADC, D and D* dataset; in particular, D has been found to have the highest significativity.
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Affiliation(s)
- Mariacristina Valerio
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Chiara Zini
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Davide Fierro
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Francesca Giura
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Anna Colarieti
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Alessandro Giuliani
- Environment and Health Dept., Istituto Superiore di Sanità, Rome, V.le Regina Elena, 299, 00161 Roma, Italy
| | - Andrea Laghi
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Carlo Catalano
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy
| | - Valeria Panebianco
- Dept. of Radiological Sciences, Oncology & Pathology-Sapienza University of Rome, V.le Regina Elena, 324, 00161 Roma, Italy.
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Malyarenko DI, Ross BD, Chenevert TL. Analysis and correction of gradient nonlinearity bias in apparent diffusion coefficient measurements. Magn Reson Med 2015; 71:1312-23. [PMID: 23794533 DOI: 10.1002/mrm.24773] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
PURPOSE Gradient nonlinearity of MRI systems leads to spatially dependent b-values and consequently high non-uniformity errors (10-20%) in apparent diffusion coefficient (ADC) measurements over clinically relevant field-of-views. This work seeks practical correction procedure that effectively reduces observed ADC bias for media of arbitrary anisotropy in the fewest measurements. METHODS All-inclusive bias analysis considers spatial and time-domain cross-terms for diffusion and imaging gradients. The proposed correction is based on rotation of the gradient nonlinearity tensor into the diffusion gradient frame where spatial bias of b-matrix can be approximated by its Euclidean norm. Correction efficiency of the proposed procedure is numerically evaluated for a range of model diffusion tensor anisotropies and orientations. RESULTS Spatial dependence of nonlinearity correction terms accounts for the bulk (75-95%) of ADC bias for FA = 0.3-0.9. Residual ADC non-uniformity errors are amplified for anisotropic diffusion. This approximation obviates need for full diffusion tensor measurement and diagonalization to derive a corrected ADC. Practical scenarios are outlined for implementation of the correction on clinical MRI systems. CONCLUSIONS The proposed simplified correction algorithm appears sufficient to control ADC non-uniformity errors in clinical studies using three orthogonal diffusion measurements. The most efficient reduction of ADC bias for anisotropic medium is achieved with non-lab-based diffusion gradients.
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