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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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Cai Y, Liu J, Yang H, Zheng L, Wu D, Xiao E, Dai Y. Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model. Med Phys 2024; 51:4635-4645. [PMID: 38753987 DOI: 10.1002/mp.17126] [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: 10/07/2023] [Revised: 04/01/2024] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). PURPOSE To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. METHODS Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. RESULTS According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471 × 10-3, 1.653 × 10-3, 9.487 × 10-3, and > 30 × 10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002). CONCLUSION The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.
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Affiliation(s)
- Yeyu Cai
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Jiayi Liu
- Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - HaiTao Yang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Liyun Zheng
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Dongmei Wu
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China
| | - Enhua Xiao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China
| | - Yongming Dai
- School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China
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Trecarten S, Sunnapwar AG, Clarke GD, Liss MA. Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions. Adv Cancer Res 2024; 161:71-118. [PMID: 39032957 DOI: 10.1016/bs.acr.2024.04.002] [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: 07/23/2024]
Abstract
PURPOSE OF REVIEW In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC. RECENT FINDINGS In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy. SUMMARY The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.
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Affiliation(s)
- Shaun Trecarten
- Department of Urology, UT Health San Antonio, San Antonio, TX, United States
| | - Abhijit G Sunnapwar
- Department of Radiology, UT Health San Antonio, San Antonio, TX, United States
| | - Geoffrey D Clarke
- Department of Radiology, UT Health San Antonio, San Antonio, TX, United States
| | - Michael A Liss
- Department of Urology, UT Health San Antonio, San Antonio, TX, United States.
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Fennessy FM, Maier SE. Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment. Eur J Radiol 2023; 167:111066. [PMID: 37651828 PMCID: PMC10623580 DOI: 10.1016/j.ejrad.2023.111066] [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: 07/05/2023] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Diffusion-weighted imaging is a dependable method for detection of clinically significant prostate cancer. In prostate tissue, there are several compartments that can be distinguished from each other, based on different water diffusion decay signals observed. Alterations in cell architecture, such as a relative increase in tumor infiltration and decrease in stroma, will influence the observed diffusion signal in a voxel due to impeded random motion of water molecules. The amount of restricted diffusion can be assessed quantitatively by measuring the apparent diffusion coefficient (ADC) value. This is traditionally calculated using a monoexponential decay formula represented by the slope of a line produced between the logarithm of signal intensity decay plotted against selected b-values. However, the choice and number of b-values and their distribution, has a significant effect on the measured ADC values. There have been many models that attempt to use higher-order functions to better describe the observed diffusion signal decay, requiring an increased number and range of b-values. While ADC can probe heterogeneity on a macroscopic level, there is a need to optimize advanced diffusion techniques to better interrogate prostate tissue microstructure. This could be of benefit in clinical challenges such as identifying sparse tumors in normal prostate tissue or better defining tumor margins. This paper reviews the principles of diffusion MRI and novel higher order diffusion signal analysis techniques to improve the detection of prostate cancer.
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Affiliation(s)
- Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
| | - Stephan E Maier
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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Andreassen MMS, Loubrie S, Tong MW, Fang L, Seibert TM, Wallace AM, Zare S, Ojeda-Fournier H, Kuperman J, Hahn M, Jerome NP, Bathen TF, Rodríguez-Soto AE, Dale AM, Rakow-Penner R. Restriction spectrum imaging with elastic image registration for automated evaluation of response to neoadjuvant therapy in breast cancer. Front Oncol 2023; 13:1237720. [PMID: 37781199 PMCID: PMC10541212 DOI: 10.3389/fonc.2023.1237720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/08/2023] [Indexed: 10/03/2023] Open
Abstract
Purpose Dynamic contrast-enhanced MRI (DCE) and apparent diffusion coefficient (ADC) are currently used to evaluate treatment response of breast cancer. The purpose of the current study was to evaluate the three-component Restriction Spectrum Imaging model (RSI3C), a recent diffusion-weighted MRI (DWI)-based tumor classification method, combined with elastic image registration, to automatically monitor breast tumor size throughout neoadjuvant therapy. Experimental design Breast cancer patients (n=27) underwent multi-parametric 3T MRI at four time points during treatment. Elastically-registered DWI images were used to generate an automatic RSI3C response classifier, assessed against manual DCE tumor size measurements and mean ADC values. Predictions of therapy response during treatment and residual tumor post-treatment were assessed using non-pathological complete response (non-pCR) as an endpoint. Results Ten patients experienced pCR. Prediction of non-pCR using ROC AUC (95% CI) for change in measured tumor size from pre-treatment time point to early-treatment time point was 0.65 (0.38-0.92) for the RSI3C classifier, 0.64 (0.36-0.91) for DCE, and 0.45 (0.16-0.75) for change in mean ADC. Sensitivity for detection of residual disease post-treatment was 0.71 (0.44-0.90) for the RSI3C classifier, compared to 0.88 (0.64-0.99) for DCE and 0.76 (0.50-0.93) for ADC. Specificity was 0.90 (0.56-1.00) for the RSI3C classifier, 0.70 (0.35-0.93) for DCE, and 0.50 (0.19-0.81) for ADC. Conclusion The automatic RSI3C classifier with elastic image registration suggested prediction of response to treatment after only three weeks, and showed performance comparable to DCE for assessment of residual tumor post-therapy. RSI3C may guide clinical decision-making and enable tailored treatment regimens and cost-efficient evaluation of neoadjuvant therapy of breast cancer.
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Affiliation(s)
- Maren M. Sjaastad Andreassen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Oncology, Vestre Viken, Drammen, Norway
| | - Stephane Loubrie
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Michelle W. Tong
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Lauren Fang
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Tyler M. Seibert
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Anne M. Wallace
- Department of Surgery, University of California, San Diego, La Jolla, CA, United States
| | - Somaye Zare
- Department of Pathology, University of California, San Diego, La Jolla, CA, United States
| | - Haydee Ojeda-Fournier
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Joshua Kuperman
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Michael Hahn
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Neil P. Jerome
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Tone F. Bathen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olav’s University Hospital, Trondheim, Norway
| | - Ana E. Rodríguez-Soto
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Conlin CC, Feng CH, Digma LA, Rodríguez-Soto AE, Kuperman JM, Rakow-Penner R, Karow DS, White NS, Seibert TM, Hahn ME, Dale AM. A Multicompartmental Diffusion Model for Improved Assessment of Whole-Body Diffusion-weighted Imaging Data and Evaluation of Prostate Cancer Bone Metastases. Radiol Imaging Cancer 2023; 5:e210115. [PMID: 36705559 PMCID: PMC9896230 DOI: 10.1148/rycan.210115] [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] [Indexed: 01/28/2023]
Abstract
Purpose To develop a multicompartmental signal model for whole-body diffusion-weighted imaging (DWI) and apply it to study the diffusion properties of normal tissue and metastatic prostate cancer bone lesions in vivo. Materials and Methods This prospective study (ClinicalTrials.gov: NCT03440554) included 139 men with prostate cancer (mean age, 70 years ± 9 [SD]). Multicompartmental models with two to four tissue compartments were fit to DWI data from whole-body scans to determine optimal compartmental diffusion coefficients. Bayesian information criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness of fit. Diffusion coefficients for the optimal model (having lowest BIC) were used to compute compartmental signal-contribution maps. The signal intensity ratio (SIR) of bone lesions to normal-appearing bone was measured on these signal-contribution maps and on conventional DWI scans and compared using paired t tests (α = .05). Two-sample t tests (α = .05) were used to compare compartmental signal fractions between lesions and normal-appearing bone. Results Lowest BIC was observed from the four-compartment model, with optimal compartmental diffusion coefficients of 0, 1.1 × 10-3, 2.8 × 10-3, and >3.0 ×10-2 mm2/sec. Fitting residuals from this model were significantly lower than from conventional apparent diffusion coefficient mapping (P < .001). Bone lesion SIR was significantly higher on signal-contribution maps of model compartments 1 and 2 than on conventional DWI scans (P < .008). The fraction of signal from compartments 2, 3, and 4 was also significantly different between metastatic bone lesions and normal-appearing bone tissue (P ≤ .02). Conclusion The four-compartment model best described whole-body diffusion properties. Compartmental signal contributions from this model can be used to examine prostate cancer bone involvement. Keywords: Whole-Body MRI, Diffusion-weighted Imaging, Restriction Spectrum Imaging, Diffusion Signal Model, Bone Metastases, Prostate Cancer Clinical trial registration no. NCT03440554 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Margolis in this issue.
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Bhuiyan EH, Dewdney A, Weinreb J, Galiana G. Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI. Med Phys 2021; 48:5804-5818. [PMID: 34287937 DOI: 10.1002/mp.15100] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 04/04/2021] [Accepted: 06/23/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times. METHODS We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients. RESULTS Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC. CONCLUSIONS Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.
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Affiliation(s)
| | | | - Jeffrey Weinreb
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - Gigi Galiana
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
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Abstract
Prostate MRI has seen increasing interest in recent years and has led to the development of new MRI techniques and sequences to improve prostate cancer (PCa) diagnosis which are reviewed in this article. Numerous studies have focused on improving image quality (segmented DWI) and faster acquisition (compressed sensing, k-t-SENSE, PROPELLER). An increasing number of studies have developed new quantitative and computer-aided diagnosis methods including artificial intelligence (PROSTATEx challenge) that mitigate the subjective nature of mpMRI interpretation. MR fingerprinting allows rapid, simultaneous generation of quantitative maps of multiple physical properties (T1, T2), where PCa are characterized by lower T1 and T2 values. New techniques like luminal water imaging (LWI), restriction spectrum imaging (RSI), VERDICT and hybrid multi-dimensional MRI (HM-MRI) have been developed for microstructure imaging, which provide information similar to histology. The distinct MR properties of tissue components and their change with the presence of cancer is used to diagnose prostate cancer. LWI is a T2-based imaging technique where long T2-component corresponding to luminal water is reduced in PCa. RSI and VERDICT are diffusion-based techniques where PCa is characterized by increased signal from intra-cellular restricted water and increased intracellular volume fraction, respectively, due to increased cellularity. VERDICT also reveal loss of extracellular-extravascular space in PCa due to loss of glandular structure. HM-MRI measures volumes of prostate tissue components, where PCa has reduced lumen and stromal and increased epithelium volume similar to results shown in histology. Similarly, molecular imaging using hyperpolarized 13C imaging has been utilized.
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Conlin CC, Feng CH, Rodriguez-Soto AE, Karunamuni RA, Kuperman JM, Holland D, Rakow-Penner R, Hahn ME, Seibert TM, Dale AM. Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models. J Magn Reson Imaging 2020; 53:628-639. [PMID: 33131186 DOI: 10.1002/jmri.27393] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 09/25/2020] [Accepted: 09/29/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Multicompartmental modeling outperforms conventional diffusion-weighted imaging (DWI) in the assessment of prostate cancer. Optimized multicompartmental models could further improve the detection and characterization of prostate cancer. PURPOSE To optimize multicompartmental signal models and apply them to study diffusion in normal and cancerous prostate tissue in vivo. STUDY TYPE Retrospective. SUBJECTS Forty-six patients who underwent MRI examination for suspected prostate cancer; 23 had prostate cancer and 23 had no detectable cancer. FIELD STRENGTH/SEQUENCE 3T multishell diffusion-weighted sequence. ASSESSMENT Multicompartmental models with 2-5 tissue compartments were fit to DWI data from the prostate to determine optimal compartmental apparent diffusion coefficients (ADCs). These ADCs were used to compute signal contributions from the different compartments. The Bayesian Information Criterion (BIC) and model-fitting residuals were calculated to quantify model complexity and goodness-of-fit. Tumor contrast-to-noise ratio (CNR) and tumor-to-background signal intensity ratio (SIR) were computed for conventional DWI and multicompartmental signal-contribution maps. STATISTICAL TESTS Analysis of variance (ANOVA) and two-sample t-tests (α = 0.05) were used to compare fitting residuals between prostate regions and between multicompartmental models. T-tests (α = 0.05) were also used to assess differences in compartmental signal-fraction between tissue types and CNR/SIR between conventional DWI and multicompartmental models. RESULTS The lowest BIC was observed from the 4-compartment model, with optimal ADCs of 5.2e-4, 1.9e-3, 3.0e-3, and >3.0e-2 mm2 /sec. Fitting residuals from multicompartmental models were significantly lower than from conventional ADC mapping (P < 0.05). Residuals were lowest in the peripheral zone and highest in tumors. Tumor tissue showed the largest reduction in fitting residual by increasing model order. Tumors had a greater proportion of signal from compartment 1 than normal tissue (P < 0.05). Tumor CNR and SIR were greater on compartment-1 signal maps than conventional DWI (P < 0.05) and increased with model order. DATA CONCLUSION The 4-compartment signal model best described diffusion in the prostate. Compartmental signal contributions revealed by this model may improve assessment of prostate cancer. Level of Evidence 3 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:628-639.
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Affiliation(s)
- Christopher C Conlin
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA
| | - Christine H Feng
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California, USA
| | - Ana E Rodriguez-Soto
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA
| | - Roshan A Karunamuni
- Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California, USA
| | - Joshua M Kuperman
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA
| | - Dominic Holland
- Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA
| | - Michael E Hahn
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA
| | - Tyler M Seibert
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA.,Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California, USA.,Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California, USA
| | - Anders M Dale
- Department of Radiology, UC San Diego School of Medicine, La Jolla, California, USA.,Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California, USA.,Halıcıoğlu Data Science Institute, UC San Diego, La Jolla, California, USA
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10
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Rourke E, Sunnapwar A, Mais D, Kukkar V, DiGiovanni J, Kaushik D, Liss MA. Inflammation appears as high Prostate Imaging-Reporting and Data System scores on prostate magnetic resonance imaging (MRI) leading to false positive MRI fusion biopsy. Investig Clin Urol 2019; 60:388-395. [PMID: 31501802 PMCID: PMC6722401 DOI: 10.4111/icu.2019.60.5.388] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 05/07/2019] [Indexed: 12/04/2022] Open
Abstract
Purpose To investigate if inflammation as a potential cause of false-positive lesions from recent UroNav magnetic resonance imaging (MRI) fusion prostate biopsy patients. Materials and Methods We retrospectively identified 43 men with 61 MRI lesions noted on prostate MRI before MRI ultrasound-guided fusion prostate biopsy. Men underwent MRI with 3T Siemens TIM Trio MRI system (Siemens AG, Germany), and lesions were identified and marked in DynaCAD system (Invivo Corporation, USA) with subsequent biopsy with MRI fusion with UroNav. We obtained targeted and standard 12-core needle biopsies. We retrospectively reviewed pathology reports for inflammation. Results We noted a total of 43 (70.5%) false-positive lesions with 28 having no cancer on any cores, and 15 lesions with cancer noted on systematic biopsy but not in the target region. Of the men with cancer, 6 of the false positive lesions had inflammation in the location of the targeted region of interest (40.0%, 6/15). However, when we examine the 21/28 lesions with an identified lesion on MRI with no cancer in all cores, 54.5% had inflammation on prostate biopsy pathology (12/22, p=0.024). We noted the highest proportion of inflammation. Conclusions Inflammation can confound the interpretation of MRI by mimicking prostate cancer. We suggested focused efforts to differentiate inflammation and cancer on prostate MRI.
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Affiliation(s)
- Elizabeth Rourke
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Abhijit Sunnapwar
- Department of Radiology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Daniel Mais
- Department of Pathology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - Vishal Kukkar
- Department of Radiology, University of Texas Health San Antonio, San Antonio, TX, USA
| | - John DiGiovanni
- University of Texas Austin, College of Pharmacy, Austin, TX, USA
| | - Dharam Kaushik
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA.,Mays Cancer Center UT Health San Antonio MD Anderson, San Antonio, TX, USA
| | - Michael A Liss
- Department of Urology, University of Texas Health San Antonio, San Antonio, TX, USA.,University of Texas Austin, College of Pharmacy, Austin, TX, USA.,Mays Cancer Center UT Health San Antonio MD Anderson, San Antonio, TX, USA
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Utility of Restriction Spectrum Imaging Among Men Undergoing First-Time Biopsy for Suspected Prostate Cancer. AJR Am J Roentgenol 2019; 213:365-370. [PMID: 31039011 DOI: 10.2214/ajr.18.20836] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE. The purpose of this article is to evaluate restriction spectrum imaging (RSI) in men undergoing MRI-ultrasound fusion biopsy for suspected prostate cancer (PCa) and to compare the performance of RSI with that of conventional DWI. MATERIALS AND METHODS. One hundred ninety-eight biopsy-naïve men enrolled in a concurrent prospective clinical trial evaluating MRI-targeted prostate biopsy underwent multiparametric MRI with RSI. Clinical and imaging features were compared between men with and without clinically significant (CS) PCa (MRI-ultrasound fusion biopsy Gleason score ≥ 3 + 4). RSI z score and apparent diffusion coefficient (ADC) were correlated, and their diagnostic performances were compared. RESULTS. CS PCa was detected in 109 of 198 men (55%). Using predefined thresholds of ADC less than or equal to 1000 μm2/s and RSI z score greater than or equal to 3, sensitivity and specificity for CS PCa were 86% and 38%, respectively, for ADC and 61% and 70%, respectively, for RSI. In the transition zone (n = 69), the sensitivity and specificity were 94% and 17%, respectively, for ADC and 59% and 69%, respectively, for RSI. Among lesions with CS PCa, RSI z score and ADC were significantly inversely correlated in the peripheral zone (ρ = -0.4852; p < 0.01) but not the transition zone (ρ = -0.2412; p = 0.17). Overall diagnostic accuracies of RSI and DWI were 0.70 and 0.68, respectively (p = 0.74). CONCLUSION. RSI and DWI achieved equivalent diagnostic performance for PCa detection in a large population of men undergoing first-time prostate biopsy for suspected PCa, but RSI had superior specificity for transition zone lesions.
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Khan UA, Rennert RC, White NS, Bartsch H, Farid N, Dale AM, Chen CC. Diagnostic utility of restriction spectrum imaging (RSI) in glioblastoma patients after concurrent radiation-temozolomide treatment: A pilot study. J Clin Neurosci 2018; 58:136-141. [PMID: 30253908 DOI: 10.1016/j.jocn.2018.09.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Revised: 05/01/2018] [Accepted: 09/10/2018] [Indexed: 01/21/2023]
Abstract
Discriminating between tumor recurrence and treatment effects in glioblastoma patients undergoing radiation-temozolomide (RT/TMZ) therapy remains a major clinical challenge. Here, we report a pilot study to determine the utility of restriction spectrum imaging (RSI), an advanced diffusion-weighted MRI (DWI) technique that affords meso-scale resolution of cell density, in this assessment. A retrospective review of 31 patients with glioblastoma treated between 2011 and 2017 who underwent surgical resection or biopsy over radiographic concern for tumor recurrence following RT/TMZ was performed. All patients underwent RSI prior to surgical resection. Diagnostic utility of RSI for tumor recurrence was determined in comparison to histopathology. Analysis of surgical specimens revealed treatment effects in 6/31 patients (19%) and tumor recurrence in 25/31 patients (81%). There was general concordance between the measured RSI signal and histopathologic diagnosis. RSI was negative in 5/6 patients (83%) in patients with histological evidence of treatment effects. RSI was positive in 21/25 patients (84%) in patients with tumor recurrence. The sensitivity, specificity, positive and negative predictive values of RSI for glioblastoma recurrence were 84%, 86%, 95%, and 60%, respectively. Histopathologic review showed agreement between the RSI signal and cellularity of the tumor specimen. These data support the use of RSI in the evaluation of treatment effects versus tumor recurrence in glioblastoma patients after RT-TMZ therapy.
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Affiliation(s)
- Usman A Khan
- Department of Neurosurgery, University of California San Diego, San Diego, CA, USA
| | - Robert C Rennert
- Department of Neurosurgery, University of California San Diego, San Diego, CA, USA
| | - Nathan S White
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
| | - Hauke Bartsch
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
| | - Nikdokht Farid
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
| | - Clark C Chen
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, USA.
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Yamin G, Schenker-Ahmed NM, Shabaik A, Adams D, Bartsch H, Kuperman J, White NS, Rakow-Penner RA, McCammack K, Parsons JK, Kane CJ, Dale AM, Karow DS. Voxel Level Radiologic-Pathologic Validation of Restriction Spectrum Imaging Cellularity Index with Gleason Grade in Prostate Cancer. Clin Cancer Res 2018; 22:2668-74. [PMID: 27250935 DOI: 10.1158/1078-0432.ccr-15-2429] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 01/05/2016] [Indexed: 11/16/2022]
Abstract
PURPOSE Restriction spectrum imaging (RSI-MRI), an advanced diffusion imaging technique, can potentially circumvent current limitations in tumor conspicuity, in vivo characterization, and location demonstrated by multiparametric magnetic resonance imaging (MP-MRI) techniques in prostate cancer detection. Prior reports show that the quantitative signal derived from RSI-MRI, the cellularity index, is associated with aggressive prostate cancer as measured by Gleason grade (GG). We evaluated the reliability of RSI-MRI to predict variance with GG at the voxel-level within clinically demarcated prostate cancer regions. EXPERIMENTAL DESIGN Ten cases were processed using whole mount sectioning after radical prostatectomy. Regions of tumor were identified by an uropathologist. Stained prostate sections were scanned at high resolution (75 μm/pixel). A grid of tiles corresponding to voxel dimensions was graded using the GG system. RSI-MRI cellularity index was calculated from presurgical prostate MR scans and presented as normalized z-score maps. In total, 2,795 tiles were analyzed and compared with RSI-MRI cellularity. RESULTS RSI-MRI cellularity index was found to distinguish between prostate cancer and benign tumor (t = 25.48, P < 0.00001). Significant differences were also found between benign tissue and prostate cancer classified as low-grade (GG = 3; t = 11.56, P < 0.001) or high-grade (GG ≥ 4; t = 24.03, P < 0.001). Furthermore, RSI-MRI differentiated between low and high-grade prostate cancer (t = 3.23; P = 0.003). CONCLUSIONS Building on our previous findings of correlation between GG and the RSI-MRI among whole tumors, our current study reveals a similar correlation at voxel resolution within tumors. Because it can detect variations in tumor grade with voxel-level precision, RSI-MRI may become an option for planning targeted procedures where identifying the area with the most aggressive disease is important. Clin Cancer Res; 22(11); 2668-74. ©2016 AACR.
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Affiliation(s)
- Ghiam Yamin
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Natalie M Schenker-Ahmed
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Ahmed Shabaik
- Department of Pathology, University of California San Diego School of Medicine, San Diego, California
| | - Dennis Adams
- Department of Pathology, University of California San Diego School of Medicine, San Diego, California
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Nathan S White
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Rebecca A Rakow-Penner
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - Kevin McCammack
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California
| | - J Kellogg Parsons
- Department of Surgery, University of California San Diego School of Medicine, San Diego, California
| | - Christopher J Kane
- Department of Surgery, University of California San Diego School of Medicine, San Diego, California
| | - Anders M Dale
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California. Department of Neurosciences, University of California, San Diego, La Jolla, California
| | - David S Karow
- Department of Radiology, University of California San Diego School of Medicine, San Diego, California.
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Abstract
A successful paradigm shift toward personalized management strategies for patients with prostate cancer (PCa) is heavily dependent on the availability of noninvasive diagnostic tools capable of accurately establishing the true extent of disease at the time of diagnosis and estimating the risk of subsequent disease progression and related mortality. Although there is still considerable scope for improvement in its diagnostic, predictive, and prognostic capabilities, multiparametric prostate magnetic resonance imaging (MRI) is currently regarded as the imaging modality of choice for local staging of PCa. A negative MRI, that is, the absence of any MRI-visible intraprostatic lesion, has a high negative predictive value for the presence of clinically significant PCa and can substantiate the consideration of active surveillance as a preferred initial management approach. MRI-derived quantitative and semi-quantitative parameters can be utilized to noninvasively characterize MRI-visible prostate lesions and identify those patients who are most likely to benefit from radical treatment, and differentiate them from patients with benign or indolent prostate pathology that may also be visible on MRI. This literature review summarizes current strategies how MRI can be used to determine a tailored management strategy for an individual patient.
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Brunsing RL, Schenker-Ahmed NM, White NS, Parsons JK, Kane C, Kuperman J, Bartsch H, Kader AK, Rakow-Penner R, Seibert TM, Margolis D, Raman SS, McDonald CR, Farid N, Kesari S, Hansel D, Shabaik A, Dale AM, Karow DS. Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI. J Magn Reson Imaging 2016; 45:323-336. [PMID: 27527500 DOI: 10.1002/jmri.25419] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 07/25/2016] [Indexed: 12/28/2022] Open
Abstract
Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting. LEVEL OF EVIDENCE 5 J. Magn. Reson. Imaging 2017;45:323-336.
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Affiliation(s)
- Ryan L Brunsing
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | | | - Nathan S White
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - J Kellogg Parsons
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Christopher Kane
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Joshua Kuperman
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Andrew Karim Kader
- Department of Surgery, University of California San Diego, San Diego, California, USA
| | - Rebecca Rakow-Penner
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Tyler M Seibert
- Department of Radiation Medicine, University of California San Diego, San Diego, California, USA
| | - Daniel Margolis
- Department of Radiology, University of California Los Angeles, Los Angeles, California, USA
| | - Steven S Raman
- Department of Radiology, University of California Los Angeles, Los Angeles, California, USA
| | - Carrie R McDonald
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Nikdokht Farid
- Department of Radiology, University of California San Diego, San Diego, California, USA
| | - Santosh Kesari
- Department of Translational Neuro-Oncology and Neurotherapeutics, Pacific Neuroscience Institute and John Wayne Cancer Institute at Providence Saint John's Health Center, Los Angeles, California, USA
| | - Donna Hansel
- Department of Pathology, University of California San Diego, San Diego, California, USA
| | - Ahmed Shabaik
- Department of Pathology, University of California San Diego, San Diego, California, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, California, USA.,Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - David S Karow
- Department of Radiology, University of California San Diego, San Diego, California, USA
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In vivo prostate cancer detection and grading using restriction spectrum imaging-MRI. Prostate Cancer Prostatic Dis 2016; 19:168-73. [PMID: 26754261 PMCID: PMC5340721 DOI: 10.1038/pcan.2015.61] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2015] [Revised: 11/19/2015] [Accepted: 11/24/2015] [Indexed: 12/31/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is emerging as a robust, noninvasive method for detecting and characterizing prostate cancer (PCa), but limitations remain in its ability to distinguish cancerous from non-cancerous tissue. We evaluated the performance of a novel MRI technique, restriction spectrum imaging (RSI-MRI), to quantitatively detect and grade PCa compared with current standard-of-care MRI. METHODS In a retrospective evaluation of 33 patients with biopsy-proven PCa who underwent RSI-MRI and standard MRI before radical prostatectomy, receiver-operating characteristic (ROC) curves were performed for RSI-MRI and each quantitative MRI term, with area under the ROC curve (AUC) used to compare each term's ability to differentiate between PCa and normal prostate. Spearman rank-order correlations were performed to assess each term's ability to predict PCa grade in the radical prostatectomy specimens. RESULTS RSI-MRI demonstrated superior differentiation of PCa from normal tissue, with AUC of 0.94 and 0.85 for RSI-MRI and conventional diffusion MRI, respectively (P=0.04). RSI-MRI also demonstrated superior performance in predicting PCa aggressiveness, with Spearman rank-order correlation coefficients of 0.53 (P=0.002) and -0.42 (P=0.01) for RSI-MRI and conventional diffusion MRI, respectively, with tumor grade. CONCLUSIONS RSI-MRI significantly improves upon current noninvasive PCa imaging and may potentially enhance its diagnosis and characterization.
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McCammack KC, Schenker-Ahmed NM, White NS, Best SR, Marks RM, Heimbigner J, Kane CJ, Parsons JK, Kuperman JM, Bartsch H, Desikan RS, Rakow-Penner RA, Liss MA, Margolis DJA, Raman SS, Shabaik A, Dale AM, Karow DS. Restriction spectrum imaging improves MRI-based prostate cancer detection. Abdom Radiol (NY) 2016; 41:946-53. [PMID: 26910114 DOI: 10.1007/s00261-016-0659-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
PURPOSE To compare the diagnostic performance of restriction spectrum imaging (RSI), with that of conventional multi-parametric (MP) magnetic resonance imaging (MRI) for prostate cancer (PCa) detection in a blinded reader-based format. METHODS Three readers independently evaluated 100 patients (67 with proven PCa) who underwent MP-MRI and RSI within 6 months of systematic biopsy (N = 67; 23 with targeting performed) or prostatectomy (N = 33). Imaging was performed at 3 Tesla using a phased-array coil. Readers used a five-point scale estimating the likelihood of PCa present in each prostate sextant. Evaluation was performed in two separate sessions, first using conventional MP-MRI alone then immediately with MP-MRI and RSI in the same session. Four weeks later, another scoring session used RSI and T2-weighted imaging (T2WI) without conventional diffusion-weighted or dynamic contrast-enhanced imaging. Reader interpretations were then compared to prostatectomy data or biopsy results. Receiver operating characteristic curves were performed, with area under the curve (AUC) used to compare across groups. RESULTS MP-MRI with RSI achieved higher AUCs compared to MP-MRI alone for identifying high-grade (Gleason score greater than or equal to 4 + 3=7) PCa (0.78 vs. 0.70 at the sextant level; P < 0.001 and 0.85 vs. 0.79 at the hemigland level; P = 0.04). RSI and T2WI alone achieved AUCs similar to MP-MRI for high-grade PCa (0.71 vs. 0.70 at the sextant level). With hemigland analysis, high-grade disease results were similar when comparing RSI + T2WI with MP-MRI, although with greater AUCs compared to the sextant analysis (0.80 vs. 0.79). CONCLUSION Including RSI with MP-MRI improves PCa detection compared to MP-MRI alone, and RSI with T2WI achieves similar PCa detection as MP-MRI.
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Affiliation(s)
- Kevin C McCammack
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Natalie M Schenker-Ahmed
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Nathan S White
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Shaun R Best
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Robert M Marks
- Department of Radiology, Naval Medical Center San Diego, San Diego, USA
| | - Jared Heimbigner
- Department of Radiology, Naval Medical Center San Diego, San Diego, USA
| | - Christopher J Kane
- Department of Urology, University of California San Diego School of Medicine, San Diego, USA
| | - J Kellogg Parsons
- Department of Urology, University of California San Diego School of Medicine, San Diego, USA
| | - Joshua M Kuperman
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Hauke Bartsch
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Rahul S Desikan
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Rebecca A Rakow-Penner
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
| | - Michael A Liss
- Department of Urology, University of Texas San Antonio School of Medicine, San Antonio, USA
| | - Daniel J A Margolis
- Department of Radiology, University of California Los Angeles Geffen School of Medicine, Los Angeles, USA
| | - Steven S Raman
- Department of Radiology, University of California Los Angeles Geffen School of Medicine, Los Angeles, USA
| | - Ahmed Shabaik
- Department of Pathology, University of California San Diego School of Medicine, San Diego, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA
- Department of Neurosciences, University of California San Diego School of Medicine, San Diego, USA
| | - David S Karow
- Department of Radiology, University of California San Diego School of Medicine, 200 W Arbor Dr, San Diego, CA, 92103, USA.
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Felker ER, Margolis DJ, Nassiri N, Marks LS. Prostate cancer risk stratification with magnetic resonance imaging. Urol Oncol 2016; 34:311-9. [PMID: 27040381 DOI: 10.1016/j.urolonc.2016.03.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2015] [Revised: 02/22/2016] [Accepted: 03/01/2016] [Indexed: 01/13/2023]
Abstract
In recent years, multiparametric magnetic resonance imaging (mpMRI) has shown promise for prostate cancer (PCa) risk stratification. mpMRI, often followed by targeted biopsy, can be used to confirm low-grade disease before enrollment in active surveillance. In patients with intermediate or high-risk PCa, mpMRI can be used to inform surgical management. mpMRI has sensitivity of 44% to 87% for detection of clinically significant PCa and negative predictive value of 63% to 98% for exclusion of significant disease. In addition to tumor identification, mpMRI has also been shown to contribute significant incremental value to currently used clinical nomograms for predicting extraprostatic extension. In combination with conventional clinical criteria, accuracy of mpMRI for prediction of extraprostatic extension ranges from 92% to 94%, significantly higher than that achieved with clinical criteria alone. Supplemental sequences, such as diffusion-weighted imaging and dynamic contrast-enhanced imaging, allow quantitative evaluation of cancer-suspicious regions. Apparent diffusion coefficient appears to be an independent predictor of PCa aggressiveness. Addition of apparent diffusion coefficient to Epstein criteria may improve sensitivity for detection of significant PCa by as much as 16%. Limitations of mpMRI include variability in reporting, underestimation of PCa volume and failure to detect clinically significant disease in a small but significant number of cases.
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Affiliation(s)
- Ely R Felker
- Department of Radiology, Ronald Reagan-UCLA Medical Center, Los Angeles, CA
| | - Daniel J Margolis
- Department of Radiology, Ronald Reagan-UCLA Medical Center, Los Angeles, CA
| | - Nima Nassiri
- Department of Urology, David Geffen School of Medicine, Los Angeles, CA
| | - Leonard S Marks
- Department of Urology, David Geffen School of Medicine, Los Angeles, CA.
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