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Dvorak AV, Kumar D, Zhang J, Gilbert G, Balaji S, Wiley N, Laule C, Moore GW, MacKay AL, Kolind SH. The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity. SCIENCE ADVANCES 2023; 9:eadh9853. [PMID: 37910622 PMCID: PMC10619933 DOI: 10.1126/sciadv.adh9853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 09/28/2023] [Indexed: 11/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) techniques are powerful tools for the study of human tissue, but, in practice, their utility has been limited by lengthy acquisition times. Here, we introduce the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework in the context of myelin water imaging (MWI); a quantitative MRI technique generally regarded as the most rigorous approach for noninvasive, in vivo measurement of myelin content. The CALIPR framework exploits data redundancy to recover high-quality images from a small fraction of an imaging dataset, which allowed MWI to be acquired with a previously unattainable sequence (fully sampled acquisition 2 hours:57 min:20 s) in 7 min:26 s (4.2% of the dataset, acceleration factor 23.9). CALIPR quantitative metrics had excellent precision (myelin water fraction mean coefficient of variation 3.2% for the brain and 3.0% for the spinal cord) and markedly increased sensitivity to demyelinating disease pathology compared to a current, widely used technique. The CALIPR framework facilitates drastically improved MWI and could be similarly transformative for other quantitative MRI applications.
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Affiliation(s)
- Adam V. Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Dushyant Kumar
- Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jing Zhang
- Global MR Applications & Workflow, GE HealthCare Canada, Mississauga, ON, Canada
| | | | - Sharada Balaji
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Neale Wiley
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - G.R. Wayne Moore
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alex L. MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H. Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
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Palombo M, Valindria V, Singh S, Chiou E, Giganti F, Pye H, Whitaker HC, Atkinson D, Punwani S, Alexander DC, Panagiotaki E. Joint estimation of relaxation and diffusion tissue parameters for prostate cancer with relaxation-VERDICT MRI. Sci Rep 2023; 13:2999. [PMID: 36810476 PMCID: PMC9943845 DOI: 10.1038/s41598-023-30182-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/17/2023] [Indexed: 02/23/2023] Open
Abstract
This work presents a biophysical model of diffusion and relaxation MRI for prostate called relaxation vascular, extracellular and restricted diffusion for cytometry in tumours (rVERDICT). The model includes compartment-specific relaxation effects providing T1/T2 estimates and microstructural parameters unbiased by relaxation properties of the tissue. 44 men with suspected prostate cancer (PCa) underwent multiparametric MRI (mp-MRI) and VERDICT-MRI followed by targeted biopsy. We estimate joint diffusion and relaxation prostate tissue parameters with rVERDICT using deep neural networks for fast fitting. We tested the feasibility of rVERDICT estimates for Gleason grade discrimination and compared with classic VERDICT and the apparent diffusion coefficient (ADC) from mp-MRI. The rVERDICT intracellular volume fraction fic discriminated between Gleason 3 + 3 and 3 + 4 (p = 0.003) and Gleason 3 + 4 and ≥ 4 + 3 (p = 0.040), outperforming classic VERDICT and the ADC from mp-MRI. To evaluate the relaxation estimates we compare against independent multi-TE acquisitions, showing that the rVERDICT T2 values are not significantly different from those estimated with the independent multi-TE acquisition (p > 0.05). Also, rVERDICT parameters exhibited high repeatability when rescanning five patients (R2 = 0.79-0.98; CV = 1-7%; ICC = 92-98%). The rVERDICT model allows for accurate, fast and repeatable estimation of diffusion and relaxation properties of PCa sensitive enough to discriminate Gleason grades 3 + 3, 3 + 4 and ≥ 4 + 3.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
- School of Computer Science and Informatics, Cardiff University, Cardiff, UK.
| | - Vanya Valindria
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Saurabh Singh
- Centre for Medical Imaging, University College London, London, UK
| | - Eleni Chiou
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Francesco Giganti
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
| | - Hayley Pye
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - Hayley C Whitaker
- Molecular Diagnostics and Therapeutics Group, Division of Surgery & Interventional Science, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Eleftheria Panagiotaki
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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The Impact of Dense Breasts on the Stage of Breast Cancer at Diagnosis: A Review and Options for Supplemental Screening. Curr Oncol 2022; 29:3595-3636. [PMID: 35621681 PMCID: PMC9140155 DOI: 10.3390/curroncol29050291] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 04/23/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
The purpose of breast cancer screening is to find cancers early to reduce mortality and to allow successful treatment with less aggressive therapy. Mammography is the gold standard for breast cancer screening. Its efficacy in reducing mortality from breast cancer was proven in randomized controlled trials (RCTs) conducted from the early 1960s to the mid 1990s. Panels that recommend breast cancer screening guidelines have traditionally relied on the old RCTs, which did not include considerations of breast density, race/ethnicity, current hormone therapy, and other risk factors. Women do not all benefit equally from mammography. Mortality reduction is significantly lower in women with dense breasts because normal dense tissue can mask cancers on mammograms. Moreover, women with dense breasts are known to be at increased risk. To provide equity, breast cancer screening guidelines should be created with the goal of maximizing mortality reduction and allowing less aggressive therapy, which may include decreasing the interval between screening mammograms and recommending consideration of supplemental screening for women with dense breasts. This review will address the issue of dense breasts and the impact on the stage of breast cancer at the time of diagnosis, and discuss options for supplemental screening.
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Chatterjee A, Antic T, Gallan AJ, Paner GP, Lin LIK, Karczmar GS, Oto A. Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists' measures. Abdom Radiol (NY) 2022; 47:801-813. [PMID: 34878579 PMCID: PMC8916544 DOI: 10.1007/s00261-021-03371-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/24/2021] [Accepted: 11/27/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To validate prostate tissue composition measured using hybrid multi-dimensional MRI (HM-MRI) by comparing with reference standard (ground truth) results from pathologists' interpretation of clinical histopathology slides following whole mount prostatectomy. MATERIALS AND METHODS 36 prospective participants with biopsy-confirmed prostate cancer underwent 3 T MRI prior to radical prostatectomy. Axial HM-MRI was acquired with all combinations of echo times of 57, 70, 150, 200 ms and b-values of 0, 150, 750, 1500 s/mm2 and data were fitted using a 3-compartment signal model using custom software to generate volumes for each tissue component (stroma, epithelium, lumen). Three experienced genitourinary pathologists independently as well as in consensus reviewed each histology image and provide an estimate of percentage of epithelium and lumen for regions-of-interest corresponding to MRI (n = 165; 64 prostate cancers and 101 benign tissue). Agreement statistics using total deviation index (TDI0.9) was performed for tissue composition measured using HM-MRI and reference standard results from pathologists' consensus. RESULTS Based on the initial results showing typical variation among pathologists TDI0.9 = 25%, we determined we will declare acceptable agreement if the 95% one-sided upper confident limit of TDI0.9 is less than 30%. The results of tissue composition measurement from HM-MRI compared to ground truth results from the consensus of 3 pathologists, reveal that ninety percent of absolute paired differences (TDI0.9) were within 18.8% and 22.4% in measuring epithelium and lumen, respectively. We are 95% confident that 90% of absolute paired differences were within 20.6% and 24.2% in measuring epithelium and lumen, respectively. These were less than our criterion of 30% and inter-pathologists' agreement (22.3% for epithelium and 24.2% for lumen) and therefore we accept the agreement performance of HM-MRI. The results revealed excellent area under the ROC curve for differentiating cancer from benign tissue based on epithelium (HM-MRI: 0.87, pathologists: 0.97) and lumen volume (HM-MRI: 0.85, pathologists: 0.77). CONCLUSION The agreement in tissue composition measurement using hybrid multidimensional MRI and consensus of pathologists is on par with the inter-raters (pathologists) agreement.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA.
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA.
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | - Alexander J Gallan
- Department of Pathology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Gladell P Paner
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | | | - Gregory S Karczmar
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, MC 2026, Chicago, IL, 60637, USA
- Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, IL, USA
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Retter A, Gong F, Syer T, Singh S, Adeleke S, Punwani S. Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly. Mol Oncol 2021; 15:2565-2579. [PMID: 34328279 PMCID: PMC8486595 DOI: 10.1002/1878-0261.13071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 07/09/2021] [Accepted: 07/29/2021] [Indexed: 12/24/2022] Open
Abstract
Imaging plays a fundamental role in all aspects of the cancer management pathway. However, conventional imaging techniques are largely reliant on morphological and size descriptors that have well-known limitations, particularly when considering targeted-therapy response monitoring. Thus, new imaging methods have been developed to characterise cancer and are now routinely implemented, such as diffusion-weighted imaging, dynamic contrast enhancement, positron emission technology (PET) and magnetic resonance spectroscopy. However, despite the improvement these techniques have enabled, limitations still remain. Novel imaging methods are now emerging, intent on further interrogating cancers. These techniques are at different stages of maturity along the biomarker pathway and aim to further evaluate the cancer microstructure (vascular, extracellular and restricted diffusion for cytometry in tumours) magnetic resonance imaging (MRI), luminal water fraction imaging] as well as the metabolic alterations associated with cancers (novel PET tracers, hyperpolarised MRI). Finally, the use of machine learning has shown powerful potential applications. By using prostate cancer as an exemplar, this Review aims to showcase these potentially potent imaging techniques and what stage we are at in their application to conventional clinical practice.
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Affiliation(s)
| | | | - Tom Syer
- UCL Centre for Medical ImagingLondonUK
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Yang L, Tan Y, Dan H, Hu L, Zhang J. Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis. Acta Radiol 2021; 62:1238-1247. [PMID: 32903025 DOI: 10.1177/0284185120956269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. PURPOSE To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. MATERIAL AND METHODS A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. RESULTS Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76-0.81), 0.85 (95% CI = 0.83-0.86), 6.58 (95% CI = 3.93-11.00), 0.24 (95% CI = 0.17-0.34), and 36.43 (95% CI = 14.41-92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. CONCLUSION Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.
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Affiliation(s)
- Lu Yang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Yuchuan Tan
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Hanli Dan
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Lin Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, No.39Shi-er-qiao Road, Chengdu, Sichuan, PR China
| | - Jiuquan Zhang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
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Meyer HJ, Wienke A, Surov A. Can dynamic contrast enhanced MRI predict gleason score in prostate cancer? a systematic review and meta analysis. Urol Oncol 2021; 39:784.e17-784.e25. [PMID: 33934966 DOI: 10.1016/j.urolonc.2021.03.015] [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: 12/17/2020] [Revised: 02/13/2021] [Accepted: 03/21/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND AND OBJECTIVES Multiparametric MRI has become a corner stone in diagnosis of prostate cancer (PC). DCE-MRI is used to quantify the influx of contrast media into tissues, which was shown to correlate with histopathology features. The present analysis sought to correlate DCE-MRI parameters with Gleason score (GS) based upon a large patient sample. MATERIAL AND METHODS MEDLINE library, Cochrane and SCOPUS databases were screened for the associations between DCE-MRI and GS in PC up to April 2020. The primary endpoint of the systematic review was the correlation between DCE-MRI parameters and GS and mean Ktrans and Kep and Ve values with standard deviation. In total, 13 studies with overall 894 patients were suitable for the analysis and included into the present study. RESULTS The highest correlation was identified for Ktrans with a pooled correlation coefficient of r = 0.36 (95% CI 0.14-0.59). A large overlap was identified between clinical significant and non-significant PC for all DCE-parameters, for Ktrans the pooled mean value of clinically non-significant PC was 0.32 min-1 [95% CI 0.13-0.51] and for clinically significant PC it was 0.45 min-1 (95% CI 0.25-0.64). CONCLUSION DCE-MRI cannot be used to predict GS in PC, and consequently cannot discriminate clinically significant from non-significant cancers.
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Affiliation(s)
- Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - Andreas Wienke
- Institute of Medical Epidemiology, Biostatistics, and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Alexey Surov
- Department of Radiology and Nuclear Medicine, University of Magdeburg, Magdeburg, Germany
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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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Doucette J, Kames C, Rauscher A. DECAES - DEcomposition and Component Analysis of Exponential Signals. Z Med Phys 2020; 30:271-278. [PMID: 32451148 DOI: 10.1016/j.zemedi.2020.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/17/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022]
Abstract
Combinations of multiple exponentially decaying signals are found across many disciplines of science. Decomposition of these multi-exponential signals into their individual components provides insight into the various contributors to the signal. Magnetic resonance images, for instance, can be acquired with multiple gradient or spin echoes to provide voxel by voxel multi-exponential T2* or T2 decays, respectively. With their millions of voxels, these images make the task of decomposition into individual exponentials computationally challenging. Current implementations take several hours, which is prohibitively long in many settings, such as on-scanner calculation for clinical applications. Here, we present a fast approach for the decomposition of multi-exponential signals. The method is applied to multi echo spin echo MRI scans and computes myelin water maps of the whole brain in under 2min, and luminal water maps of the prostate in under 1min.
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Affiliation(s)
- Jonathan Doucette
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada.
| | - Christian Kames
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada
| | - Alexander Rauscher
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Road, V6T 1Z1 Vancouver, BC, Canada; UBC MRI Research Centre, 2221 Wesbrook Mall, V6T 2B5 Vancouver, BC, Canada; Department of Radiology, University of British Columbia, 2775 Laurel Street, V5Z 1M9 Vancouver, BC, Canada; Department of Pediatrics, Faculty of Medicine, University of British Columbia, 4480 Oak Street, BC Children's Hospital, V6H 3V4 Vancouver, BC, Canada
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Hectors SJ, Said D, Gnerre J, Tewari A, Taouli B. Luminal Water Imaging: Comparison With Diffusion-Weighted Imaging (DWI) and PI-RADS for Characterization of Prostate Cancer Aggressiveness. J Magn Reson Imaging 2020; 52:271-279. [PMID: 31961049 DOI: 10.1002/jmri.27050] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/14/2019] [Accepted: 12/16/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Luminal water imaging (LWI), a multicomponent T2 mapping technique, has shown promise for prostate cancer (PCa) detection and characterization. PURPOSE To 1) quantify LWI parameters and apparent diffusion coefficient (ADC) in PCa and benign peripheral zone (PZ) tissues; and 2) evaluate the diagnostic performance of LWI, ADC, and PI-RADS parameters for differentiation between low- and high-grade PCa lesions. STUDY TYPE Prospective. SUBJECTS Twenty-six PCa patients undergoing prostatectomy (mean age 59 years, range 46-72 years). FIELD STRENGTH/SEQUENCE Multiparametric MRI at 3.0T, including diffusion-weighted imaging (DWI) and LWI T2 mapping. ASSESSMENT LWI parameters and ADC were quantified in index PCa lesions and benign PZ. STATISTICAL TESTS Differences in MRI parameters between PCa and benign PZ were assessed using Wilcoxon signed tests. Spearman correlation of pathological grade group (GG) with LWI parameters, ADC, and PI-RADS was evaluated. The utility of each of the parameters for differentiation between low-grade (GG ≤2) and high-grade (GG ≥3) PCa was determined by Mann-Whitney U tests and ROC analyses. RESULTS Twenty-six index lesions were analyzed (mean size 1.7 ± 0.8 cm, GG: 1 [n = 1; 4%], 2 [n = 14, 54%], 3 [n = 8, 31%], 5 [n = 3, 12%]). LWI parameters and ADC both showed high diagnostic performance for differentiation between benign PZ and PCa (highest area under the curve [AUC] for LWI parameter T2,short [AUC = 0.98, P < 0.001]). The LWI parameters luminal water fraction (LWF) and amplitude of long T2 component Along significantly correlated with GG (r = -0.441, P = 0.024 and r = -0.414, P = 0.036, respectively), while PI-RADS, ADC, and the other LWI parameters did not (P = 0.132-0.869). LWF and Along also showed significant differences between low-grade and high-grade PCa (AUC = 0.776, P = 0.008 and AUC = 0.758, P = 0.027, respectively). Maximum diagnostic performance for discrimination of high-grade PCa was found with combined LWI parameters (AUC 0.891, P = 0.001). DATA CONCLUSION LWI parameters, in particular in combination, showed superior diagnostic performance for differentiation between low-grade and high-grade PCa compared to ADC and PI-RADS assessment. J. Magn. Reson. Imaging 2020;52:271-279.
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Affiliation(s)
- Stefanie J Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Weill Cornell Medicine, New York, New York, USA
| | - Daniela Said
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Universidad de los Andes, Santiago, Chile
| | - Jeffrey Gnerre
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ashutosh Tewari
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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