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Habrich J, Boeke S, Fritz V, Koerner E, Nikolaou K, Schick F, Gani C, Zips D, Thorwarth D. Reproducibility of diffusion-weighted magnetic resonance imaging in head and neck cancer assessed on a 1.5 T MR-Linac and comparison to parallel measurements on a 3 T diagnostic scanner. Radiother Oncol 2024; 191:110046. [PMID: 38070687 DOI: 10.1016/j.radonc.2023.110046] [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: 05/25/2023] [Revised: 11/27/2023] [Accepted: 12/03/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND AND PURPOSE Before quantitative imaging biomarkers (QIBs) acquired with magnetic resonance imaging (MRI) can be used for interventional trials in radiotherapy (RT), technical validation of these QIBs is necessary. The aim of this study was to assess the reproducibility of apparent diffusion coefficient (ADC) values, derived from diffusion-weighted (DW) MRI, in head and neck cancer using a 1.5 T MR-Linac (MRL) by comparison to a 3 T diagnostic scanner (DS). MATERIAL AND METHODS DW-MRIs were acquired on MRL and DS for 15 head and neck cancer patients before RT and in week 2 and rigidly registered to the planning computed tomography. Mean ADC values were calculated for submandibular (SG) and parotid (PG) glands as well as target volumes (TV, gross tumor volume and lymph nodes), which were delineated based on computed tomography. Mean absolute ADC differences as well as within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs) were calculated for all volumes of interest. RESULTS A total of 23 datasets were analyzed. Mean ADC difference (DS-MRL) for SG, PG and TV resulted in 142, 254 and 93·10-6 mm2/s. wCVs/ICCs, comparing MRL and DS, were determined as 13.7 %/0.26, 24.4 %/0.23 and 16.1 %/0.73 for SG, PG and TV, respectively. CONCLUSION ADC values, measured on the 1.5 T MRL, showed reasonable reproducibility with an ADC underestimation in contrast to the DS. This ADC shift must be validated in further experiments and considered for future translation of QIB candidates from DS to MRL for response adaptive RT.
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Affiliation(s)
- Jonas Habrich
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany.
| | - Simon Boeke
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Victor Fritz
- Section for Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Elisa Koerner
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Fritz Schick
- Section for Experimental Radiology, Department of Diagnostic and Interventional Radiology, University of Tübingen, Germany
| | - Cihan Gani
- Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Daniel Zips
- German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; Department of Radiation Oncology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany; German Cancer Consortium (DKTK), partner site Tübingen, and German Cancer Research Center (DKFZ), Heidelberg, Germany
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Zhuang H, Chatterjee A, Fan X, Qi S, Qian W, He D. A radiomics based method for prediction of prostate cancer Gleason score using enlarged region of interest. BMC Med Imaging 2023; 23:205. [PMID: 38066434 PMCID: PMC10709874 DOI: 10.1186/s12880-023-01167-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Prostate cancer (PCa) is one of the most common cancers in men worldwide, and its timely diagnosis and treatment are becoming increasingly important. MRI is in increasing use to diagnose cancer and to distinguish between non-clinically significant and clinically significant PCa, leading to more precise diagnosis and treatment. The purpose of this study is to present a radiomics-based method for determining the Gleason score (GS) for PCa using tumour heterogeneity on multiparametric MRI (mp-MRI). METHODS Twenty-six patients with biopsy-proven PCa were included in this study. The quantitative T2 values, apparent diffusion coefficient (ADC) and signal enhancement rates (α) were calculated using multi-echo T2 images, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI), for the annotated region of interests (ROI). After texture feature analysis, ROI range expansion and feature filtering was performed. Then obtained data were put into support vector machine (SVM), K-Nearest Neighbor (KNN) and other classifiers for binary classification. RESULTS The highest classification accuracy was 73.96% for distinguishing between clinically significant (Gleason 3 + 4 and above) and non-significant cancers (Gleason 3 + 3) and 83.72% for distinguishing between Gleason 3 + 4 from Gleason 4 + 3 and above, which was achieved using initial ROIs drawn by the radiologists. The accuracy improved when using expanded ROIs to 80.67% using SVM and 88.42% using Bayesian classification for distinguishing between clinically significant and non-significant cancers and Gleason 3 + 4 from Gleason 4 + 3 and above, respectively. CONCLUSIONS Our results indicate the research significance and value of this study for determining the GS for prostate cancer using the expansion of the ROI region.
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Affiliation(s)
- Haoming Zhuang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Aritrick Chatterjee
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
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Møller JM, Boesen L, Hansen AE, Kettles K, Løgager V. Quantification of cross-vendor variation in ADC measurements in vendor-specific prostate MRI-protocols. Eur J Radiol 2023; 165:110942. [PMID: 37364483 DOI: 10.1016/j.ejrad.2023.110942] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/14/2023] [Accepted: 06/19/2023] [Indexed: 06/28/2023]
Abstract
PURPOSE The purpose of this study was to quantify the variability of Apparent Diffusion Coefficient (ADC) and test if there were statistically significant differences in ADC between MRI systems and sequences. METHOD With a two-chamber cylindrical ADC phantom with fixed ADC values (1,000 and 1,600x10-6 mm2/s) a single-shot (ss) Echo Planar Imaging (EPI), a multi-shot EPI, a reduced field of view DWI (zoom) and a Turbo Spin Echo DWI sequence were tested in six MRI systems from three vendors at 1.5 T and 3 T. Technical parameters were according to Prostate Imaging Reporting and Data System Version 2.1. ADC maps were calculated by vendor specific algorithms. Absolute and relative differences in ADC from the phantom-ADC were calculated and differences between sequences were tested. RESULTS At 3 T absolute differences from phantom given ADC (∼1,000 and ∼ 1,600x10-6 mm2/s) were -83 - 42x10-6 mm2/s (-8.3%-4.2%) and -48 - 15x10-6 mm2/s (-3%-0.9%), respectively and at 1.5 T absolute differences were -81 - 26x10-6 mm2/s (-2.6%-8.1%) and -74 - 67x10-6 mm2/s (-4.6%-4.2%), respectively. Significant statistical differences in ADC measurements were identified between vendors in all sequences except for ssEPI and zoom at 3 T in the 1,600x10-6 mm2/s phantom chamber. Significant differences were also identified between ADC measurements at 1.5 T and 3 T in some of the sequences and vendors, but not all. CONCLUSION The variation of ADC between different MRI systems and prostate specific DWI sequences is limited in this phantom study and without apparent clinical relevance. However, prospective multicenter studies of prostate cancer patients are needed for further investigation.
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Affiliation(s)
- Jakob M Møller
- Dep. of Radiology, Copenhagen University Hospital, Herlev-Gentofte, Denmark, Borgmester Ib Juuls vej 17, DK-2730 Herlev, Denmark.
| | - Lars Boesen
- Dep. of Urology, Copenhagen University Hospital, Herlev-Gentofte, Denmark
| | - Adam Espe Hansen
- Dep of radiology, Copenhagen University Hospital, Rigshospitalet and dep. of clinical medicine Copenhagen University, Copenhagen, Denmark
| | | | - Vibeke Løgager
- Dep. of Radiology, Copenhagen University Hospital, Herlev-Gentofte, Denmark
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Rogers HJ, Singh S, Barnes A, Obuchowski NA, Margolis DJ, Malyarenko DI, Chenevert TL, Shukla-Dave A, Boss MA, Punwani S. Test-retest repeatability of ADC in prostate using the multi b-Value VERDICT acquisition. Eur J Radiol 2023; 162:110782. [PMID: 37004362 PMCID: PMC10334409 DOI: 10.1016/j.ejrad.2023.110782] [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: 01/19/2023] [Revised: 02/24/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023]
Abstract
PURPOSE VERDICT (Vascular, Extracellular, Restricted Diffusion for Cytometry in Tumours) MRI is a multi b-value, variable diffusion time DWI sequence that allows generation of ADC maps from different b-value and diffusion time combinations. The aim was to assess precision of prostate ADC measurements from varying b-value combinations using VERDICT and determine which protocol provides the most repeatable ADC. MATERIALS AND METHODS Forty-one men (median age: 67.7 years) from a prior prospective VERDICT study (April 2016-October 2017) were analysed retrospectively. Men who were suspected of prostate cancer and scanned twice using VERDICT were included. ADC maps were formed using 5b-value combinations and the within-subject standard deviations (wSD) were calculated per ADC map. Three anatomical locations were analysed per subject: normal TZ (transition zone), normal PZ (peripheral zone), and index lesions. Repeated measures ANOVAs showed which b-value range had the lowest wSD, Spearman correlation and generalized linear model regression analysis determined whether wSD was related to ADC magnitude and ROI size. RESULTS The mean lesion ADC for b0b1500 had the lowest wSD in most zones (0.18-0.58x10-4 mm2/s). The wSD was unaffected by ADC magnitude (Lesion: p = 0.064, TZ: p = 0.368, PZ: p = 0.072) and lesion Likert score (p = 0.95). wSD showed a decrease with ROI size pooled over zones (p = 0.019, adjusted regression coefficient = -1.6x10-3, larger ROIs for TZ versus PZ versus lesions). ADC maps formed with a maximum b-value of 500 s/mm2 had the largest wSDs (1.90-10.24x10-4 mm2/s). CONCLUSION ADC maps generated from b0b1500 have better repeatability in normal TZ, normal PZ, and index lesions.
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Affiliation(s)
- Harriet J Rogers
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK.
| | - Saurabh Singh
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Anna Barnes
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | | | | | - Amita Shukla-Dave
- Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michael A Boss
- Center for Research and Innovation, American College of Radiology, Philadelphia, PA, USA
| | - Shonit Punwani
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
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Singh D, Das CJ, Kumar V, Singh A, Mehndiratta A. Quantification of prostate tumour diameter and volume from MR images using 3D ellipsoid model and its impact on PI-RADS v2.1 assessment. Sci Rep 2022; 12:21501. [PMID: 36513800 PMCID: PMC9748032 DOI: 10.1038/s41598-022-26065-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Maximum diameter and volume of the tumour provide important clinical information and are decision-making parameters for patients suspected with prostate cancer (PCa). The objectives of this study were to develop an automated method for 3D tumour measurement and compare it with the radiologist's manual assessment, as well as to investigate the impact of 3D tumour measurement on Prostate Imaging-Reporting and Data System version-2.1 (PI-RADS v2.1) scoring of prostate cancer. Tumour maximum diameter and volume were calculated using automated ellipsoid-fit method. For all PI-RADS scores, mean ± standard deviation range of tumour maximum diameter and volume measured using ellipsoid-fit method were 1.36 ± 0.28 to 1.97 ± 0.67 cm and 0.49 ± 0.31 to 1.05 ± 0.78 cc and manual assessment were in range of 0.73 ± 0.12 to 1.14 ± 0.25 cm and 0.36 ± 0.21 to 0.93 ± 0.39 cc, respectively. Ellipsoid-fit method showed significantly (p < 0.05) higher values for maximum diameter and volume than manual assessment. 3D measurement of tumour using ellipsoid-fit method was found to have higher maximum diameter and volume values (in 40-61% patients) compared to conventional assessment by radiologist, which may have an impact on PI-RADS v2.1 scoring system.
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Affiliation(s)
- Dharmesh Singh
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Chandan J. Das
- grid.413618.90000 0004 1767 6103Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Virendra Kumar
- grid.413618.90000 0004 1767 6103Department of NMR, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India ,grid.413618.90000 0004 1767 6103Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India ,grid.413618.90000 0004 1767 6103Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India ,grid.417967.a0000 0004 0558 8755Centre for Biomedical Engineering, IIT Delhi Hauz-Khas, Room No-298, Block III, New Delhi, 110016 India
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Machine learning-based analysis of a semi-automated PI-RADS v2.1 scoring for prostate cancer. Front Oncol 2022; 12:961985. [PMID: 36505875 PMCID: PMC9730331 DOI: 10.3389/fonc.2022.961985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 10/27/2022] [Indexed: 11/27/2022] Open
Abstract
Background Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS v2.1) was developed to standardize the interpretation of multiparametric MRI (mpMRI) for prostate cancer (PCa) detection. However, a significant inter-reader variability among radiologists has been found in the PI-RADS assessment. The purpose of this study was to evaluate the diagnostic performance of an in-house developed semi-automated model for PI-RADS v2.1 scoring using machine learning methods. Methods The study cohort included an MRI dataset of 59 patients (PI-RADS v2.1 score 2 = 18, score 3 = 10, score 4 = 16, and score 5 = 15). The proposed semi-automated model involved prostate gland and zonal segmentation, 3D co-registration, lesion region of interest marking, and lesion measurement. PI-RADS v2.1 scores were assessed based on lesion measurements and compared with the radiologist PI-RADS assessment. Machine learning methods were used to evaluate the diagnostic accuracy of the proposed model by classification of PI-RADS v2.1 scores. Results The semi-automated PI-RADS assessment based on the proposed model correctly classified 50 out of 59 patients and showed a significant correlation (r = 0.94, p < 0.05) with the radiologist assessment. The proposed model achieved an accuracy of 88.00% ± 0.98% and an area under the receiver-operating characteristic curve (AUC) of 0.94 for score 2 vs. score 3 vs. score 4 vs. score 5 classification and accuracy of 93.20 ± 2.10% and AUC of 0.99 for low score vs. high score classification using fivefold cross-validation. Conclusion The proposed semi-automated PI-RADS v2.1 assessment system could minimize the inter-reader variability among radiologists and improve the objectivity of scoring.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of Nuclear Magnetic Resonance (NMR), All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J. Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India,Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India,*Correspondence: Amit Mehndiratta,
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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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Impact of measurement method on interobserver variability of apparent diffusion coefficient of lesions in prostate MRI. PLoS One 2022; 17:e0268829. [PMID: 35604891 PMCID: PMC9126398 DOI: 10.1371/journal.pone.0268829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 05/09/2022] [Indexed: 11/28/2022] Open
Abstract
Purpose To compare the inter-observer variability of apparent diffusion coefficient (ADC) values of prostate lesions measured by 2D-region of interest (ROI) with and without specific measurement instruction. Methods Forty lesions in 40 patients who underwent prostate MR followed by targeted prostate biopsy were evaluated. A multi-reader study (10 readers) was performed to assess the agreement of ADC values between 2D-ROI without specific instruction and 2D-ROI with specific instruction to place a 9-pixel size 2D-ROI covering the lowest ADC area. The computer script generated multiple overlapping 9-pixel 2D-ROIs within a 3D-ROI encompassing the entire lesion placed by a single reader. The lowest mean ADC values from each 2D-small-ROI were used as reference values. Inter-observer agreement was assessed using the Bland-Altman plot. Intraclass correlation coefficient (ICC) was assessed between ADC values measured by 10 readers and the computer-calculated reference values. Results Ten lesions were benign, 6 were Gleason score 6 prostate carcinoma (PCa), and 24 were clinically significant PCa. The mean±SD ADC reference value by 9-pixel-ROI was 733 ± 186 (10−6 mm2/s). The 95% limits of agreement of ADC values among readers were better with specific instruction (±112) than those without (±205). ICC between reader-measured ADC values and computer-calculated reference values ranged from 0.736–0.949 with specific instruction and 0.349–0.919 without specific instruction. Conclusion Interobserver agreement of ADC values can be improved by indicating a measurement method (use of a specific ROI size covering the lowest ADC area).
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Hu L, Zhou DW, Fu CX, Benkert T, Xiao YF, Wei LM, Zhao JG. Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study. Front Oncol 2021; 11:697721. [PMID: 34568027 PMCID: PMC8458902 DOI: 10.3389/fonc.2021.697721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/11/2021] [Indexed: 11/29/2022] Open
Abstract
Background Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value. Objectives We aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks. Methods This prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient. Results The s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC. Conclusion The deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.
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Affiliation(s)
- Lei Hu
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Da Wei Zhou
- State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, Xi'an, China
| | - Cai Xia Fu
- Magnetic Resonance (MR) Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Yun Feng Xiao
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Li Ming Wei
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Jun Gong Zhao
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring. NMR IN BIOMEDICINE 2021; 34:e4495. [PMID: 33638244 DOI: 10.1002/nbm.4495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of NMR, All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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11
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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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12
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Chatterjee A, Nolan P, Sun C, Mathew M, Dwivedi D, Yousuf A, Antic T, Karczmar GS, Oto A. Effect of Echo Times on Prostate Cancer Detection on T2-Weighted Images. Acad Radiol 2020; 27:1555-1563. [PMID: 31992480 PMCID: PMC7381367 DOI: 10.1016/j.acra.2019.12.014] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/27/2019] [Accepted: 12/17/2019] [Indexed: 02/05/2023]
Abstract
PURPOSE To compare the effect of different echo times (TE) on the detection of prostate cancer (PCa) on T2-weighted MR images. MATERIALS AND METHODS This study recruited patients (n = 38) with histologically confirmed PCa who underwent preoperative 3T MRI. Three radiologists independently marked region on interests (ROIs) on suspected PCa lesions on T2-weighted images at different TEs: 90, 150, and 180 ms obtained with Turbo Spin Echo imaging protocol with multiple echoes. The ROIs were assigned a value 1-5 indicating the reviewer's confidence in accurately detecting PCa. These ROIs were compared to histologically confirmed PCa (n = 95) on whole mount prostatectomy sections to calculate sensitivity, positive predictive value (PPV), and confidence score. RESULTS Two radiologists (R1, R2) showed significantly increased sensitivity for PCa detection at 180 ms TE compared to 90 ms (R1: 43.2, 50.5, 50.5%, R2: 45.3, 44.2, 53.7% at TE of 90, 150, 180 ms, respectively) (p = 0.048, 0.033 for R1 and R2). Sensitivity was similar for radiologist 3 (45.3%-46.3%) at different TE values (p = 0.953). No significant difference in the PPV (R1: 64.1%-70.6%, R2: 46.7%-56.0%, R3: 70.5%-81.5%) and the confidence score assigned (R1: 4.6-4.8, R2: 4.6-4.8 R3: 4.3-4.4) was found for either of the radiologists. CONCLUSION Our results suggest improved detection of PCa with similar PPV and confidence scores when higher TE values are utilized for T2-weighted image acquisition.
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Affiliation(s)
- Aritrick Chatterjee
- Department of Radiology, University of Chicago, Chicago, IL, USA,Sanford Grossman Prostate Imaging and Image Guided Therapy Center, University of Chicago, Chicago, IL, USA
| | - Paul Nolan
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Chongpeng Sun
- Department of Radiology, University of Chicago, Chicago, IL, USA,Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Melvy Mathew
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Durgesh Dwivedi
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, IL, USA,Sanford Grossman Prostate Imaging and Image Guided Therapy Center, University of Chicago, Chicago, IL, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, USA
| | - Gregory S. Karczmar
- Department of Radiology, University of Chicago, Chicago, IL, USA,Sanford Grossman Prostate Imaging and Image Guided Therapy Center, University of Chicago, Chicago, IL, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637; Sanford J. Grossman Center of Excellence in Prostate Imaging and Image Guided Therapy, University of Chicago, Chicago, Illinois.
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13
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McGarry SD, Bukowy JD, Iczkowski KA, Lowman AK, Brehler M, Bobholz S, Nencka A, Barrington A, Jacobsohn K, Unteriner J, Duvnjak P, Griffin M, Hohenwalter M, Keuter T, Huang W, Antic T, Paner G, Palangmonthip W, Banerjee A, LaViolette PS. Radio-pathomic mapping model generated using annotations from five pathologists reliably distinguishes high-grade prostate cancer. J Med Imaging (Bellingham) 2020; 7:054501. [PMID: 32923510 PMCID: PMC7479263 DOI: 10.1117/1.jmi.7.5.054501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 08/20/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose: Our study predictively maps epithelium density in magnetic resonance imaging (MRI) space while varying the ground truth labels provided by five pathologists to quantify the downstream effects of interobserver variability. Approach: Clinical imaging and postsurgical tissue from 48 recruited prospective patients were used in our study. Tissue was sliced to match the MRI orientation and whole-mount slides were stained and digitized. Data from 28 patients ( n = 33 slides) were sent to five pathologists to be annotated. Slides from the remaining 20 patients ( n = 123 slides) were annotated by one of the five pathologists. Interpathologist variability was measured using Krippendorff's alpha. Pathologist-specific radiopathomic mapping models were trained using a partial least-squares regression using MRI values to predict epithelium density, a known marker for disease severity. An analysis of variance characterized intermodel means difference in epithelium density. A consensus model was created and evaluated using a receiver operator characteristic classifying high grade versus low grade and benign, and was statistically compared to apparent diffusion coefficient (ADC). Results: Interobserver variability ranged from low to acceptable agreement (0.31 to 0.69). There was a statistically significant difference in mean predicted epithelium density values ( p < 0.001 ) between the five models. The consensus model outperformed ADC (areas under the curve = 0.80 and 0.71, respectively, p < 0.05 ). Conclusion: We demonstrate that radiopathomic maps of epithelium density are sensitive to the pathologist annotating the dataset; however, it is unclear if these differences are clinically significant. The consensus model produced the best maps, matched the performance of the best individual model, and outperformed ADC.
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Affiliation(s)
- Sean D McGarry
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - John D Bukowy
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth A Iczkowski
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States
| | - Allison K Lowman
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Brehler
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Samuel Bobholz
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - Andrew Nencka
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Alex Barrington
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth Jacobsohn
- Medical College of Wisconsin, Department of Urological Surgery, Milwaukee, Wisconsin, United States
| | - Jackson Unteriner
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Petar Duvnjak
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Michael Griffin
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Mark Hohenwalter
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Tucker Keuter
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Wei Huang
- University of Wisconsin-Madison, Department of Pathology, Madison, Wisconsin, United States
| | - Tatjana Antic
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Gladell Paner
- University of Chicago, Department of Pathology, Chicago, Illinois, United States
| | - Watchareepohn Palangmonthip
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Chiang Mai University, Department of Pathology, Faculty of Medicine, Chiang Mai, Thailand
| | - Anjishnu Banerjee
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - Peter S LaViolette
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, Wisconsin, United States
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14
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Schieda N, Lim CS, Zabihollahy F, Abreu-Gomez J, Krishna S, Woo S, Melkus G, Ukwatta E, Turkbey B. Quantitative Prostate MRI. J Magn Reson Imaging 2020; 53:1632-1645. [PMID: 32410356 DOI: 10.1002/jmri.27191] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 12/17/2022] Open
Abstract
Prostate MRI is reported in clinical practice using the Prostate Imaging and Data Reporting System (PI-RADS). PI-RADS aims to standardize, as much as possible, the acquisition, interpretation, reporting, and ultimately the performance of prostate MRI. PI-RADS relies upon mainly subjective analysis of MR imaging findings, with very few incorporated quantitative features. The shortcomings of PI-RADS are mainly: low-to-moderate interobserver agreement and modest accuracy for detection of clinically significant tumors in the transition zone. The use of a more quantitative analysis of prostate MR imaging findings is therefore of interest. Quantitative MR imaging features including: tumor size and volume, tumor length of capsular contact, tumor apparent diffusion coefficient (ADC) metrics, tumor T1 and T2 relaxation times, tumor shape, and texture analyses have all shown value for improving characterization of observations detected on prostate MRI and for differentiating between tumors by their pathological grade and stage. Quantitative analysis may therefore improve diagnostic accuracy for detection of cancer and could be a noninvasive means to predict patient prognosis and guide management. Since quantitative analysis of prostate MRI is less dependent on an individual users' assessment, it could also improve interobserver agreement. Semi- and fully automated analysis of quantitative (radiomic) MRI features using artificial neural networks represent the next step in quantitative prostate MRI and are now being actively studied. Validation, through high-quality multicenter studies assessing diagnostic accuracy for clinically significant prostate cancer detection, in the domain of quantitative prostate MRI is needed. This article reviews advances in quantitative prostate MRI, highlighting the strengths and limitations of existing and emerging techniques, as well as discussing opportunities and challenges for evaluation of prostate MRI in clinical practice when using quantitative assessment. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Christopher S Lim
- Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Ontario, Canada
| | | | - Jorge Abreu-Gomez
- Department of Medical Imaging, Sunnybrook Health Sciences, Toronto, Ontario, Canada
| | - Satheesh Krishna
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Gerd Melkus
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Eran Ukwatta
- Faculty of Engineering, Guelph University, Guelph, Ontario, Canada
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute NIH, Bethesda, Maryland, USA
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15
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Yuan Q, Pedrosa I. Editorial for "Luminal Water Imaging: Comparison With Diffusion-Weighted Imaging (DWI) and PI-RADS for Characterization of Prostate Cancer Aggressiveness". J Magn Reson Imaging 2020; 52:280-281. [PMID: 32227379 DOI: 10.1002/jmri.27152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 03/13/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Qing Yuan
- 1Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Ivan Pedrosa
- 1Department of Radiology, UT Southwestern Medical Center, Dallas, Texas, USA.,2Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA.,3Department of Urology, UT Southwestern Medical Center, Dallas, Texas, USA
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16
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He Y, Rong Y, Chen H, Zhang Z, Qiu J, Zheng L, Benedict S, Niu X, Pan N, Liu Y, Yuan Z. Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer. Acta Radiol 2020; 61:568-576. [PMID: 31466457 DOI: 10.1177/0284185119870157] [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: 12/26/2022]
Abstract
Background The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature. Purpose To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer. Material and Methods Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group. Results Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix. Conclusions Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.
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Affiliation(s)
- Yaoyao He
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
- Medical Engineering and Technology Center, Taishan Medical University, Taian, PR China
| | - Yi Rong
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, USA
| | - Hao Chen
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zhaoxi Zhang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Jianfeng Qiu
- Medical Engineering and Technology Center, Taishan Medical University, Taian, PR China
| | - Lili Zheng
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Stanley Benedict
- Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, USA
| | - Xiaohui Niu
- College of Informatics, Huazhong Agricultural University, Wuhan, PR China
| | - Ning Pan
- College of Biomedical Engineering, South Central University for Nationalities, Wuhan, PR China
- Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan, PR China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
| | - Zilong Yuan
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, PR China
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17
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Synthetic Apparent Diffusion Coefficient for High b-Value Diffusion-Weighted MRI in Prostate. Prostate Cancer 2020; 2020:5091218. [PMID: 32095289 PMCID: PMC7035570 DOI: 10.1155/2020/5091218] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 01/09/2020] [Accepted: 01/16/2020] [Indexed: 02/07/2023] Open
Abstract
Purpose It has been reported that diffusion-weighted imaging (DWI) with ultrahigh b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher Materials and Methods. Fifteen patients (7 malignant and 8 benign) were included in this study retrospectively with the institutional ethical committee approval. All images were acquired at a 3T MR scanner. The ADC values were calculated using a monoexponential model. Synthetic ADC (sADC) for higher b-value increases the diagnostic power of prostate cancer. DWI with higher Results No significant difference was observed between actual ADC and sADC for b-value increases the diagnostic power of prostate cancer. DWI with higher p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (p=0.002, paired t-test) in sDWI as compared to DWI. Malignant lesions showed significantly lower sADC as compared to benign lesions (Discussion/ Conclusion Our initial investigation suggests that the ADC values corresponding to higher b-value can be computed using log-linear relationship derived from lower b-values (b ≤ 1000). Our method might help clinicians to decide the optimal b-value for prostate lesion identification.b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher b-value increases the diagnostic power of prostate cancer. DWI with higher
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18
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Chatterjee A, He D, Fan X, Antic T, Jiang Y, Eggener S, Karczmar GS, Oto A. Diagnosis of Prostate Cancer by Use of MRI-Derived Quantitative Risk Maps: A Feasibility Study. AJR Am J Roentgenol 2019; 213:W66-W75. [PMID: 31039019 DOI: 10.2214/ajr.18.20702] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE. The purpose of this study was to develop a new quantitative image analysis tool for estimating the risk of cancer of the prostate by use of quantitative multiparametric MRI (mpMRI) metrics. MATERIALS AND METHODS. Thirty patients with biopsy-confirmed prostate cancer (PCa) who underwent preoperative 3-T mpMRI were included in the study. Quantitative mpMRI metrics-apparent diffusion coefficient (ADC), T2, and dynamic contrast-enhanced (DCE) signal enhancement rate (α)-were calculated on a voxel-by-voxel basis for the whole prostate and coregistered. A normalized risk value (0-100) for each mpMRI parameter was obtained, with high risk values associated with low T2 and ADC and high signal enhancement rate. The final risk score was calculated as a weighted sum of the risk scores (ADC, 40%; T2, 40%; DCE, 20%). Data from five patients were used as training set to find the threshold for predicting PCa. In the other 25 patients, any region with a minimum of 30 con-joint voxels (≈ 4.8 mm2) with final risk score above the threshold was considered positive for cancer. Lesion-based and sector-based analyses were performed by matching prostatectomyverified malignancy and PCa predicted with the risk analysis tool. RESULTS. The risk map tool had sensitivity of 76.6%, 89.2%, and 100% for detecting all lesions, clinically significant lesions (≥ Gleason 3 + 4), and index lesions, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for PCa detection for all lesions in the sector-based analysis were 78.9%, 88.5%, 84.4%, and 84.1%, respectively, with an ROC AUC of 0.84. CONCLUSION. The risk analysis tool is effective for detecting clinically significant PCa with reasonable sensitivity and specificity in both peripheral and transition zones.
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Affiliation(s)
- Aritrick Chatterjee
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
| | - Dianning He
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
- 2 Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, China
| | - Xiaobing Fan
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
| | - Tatjana Antic
- 3 Department of Pathology, University of Chicago, Chicago, IL
| | - Yulei Jiang
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
| | - Scott Eggener
- 4 Department of Urology, University of Chicago, Chicago, IL
| | - Gregory S Karczmar
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
| | - Aytekin Oto
- 1 Department of Radiology, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637
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19
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Barrett T, Rajesh A, Rosenkrantz AB, Choyke PL, Turkbey B. PI-RADS version 2.1: one small step for prostate MRI. Clin Radiol 2019; 74:841-852. [PMID: 31239107 DOI: 10.1016/j.crad.2019.05.019] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2019] [Accepted: 05/30/2019] [Indexed: 12/19/2022]
Abstract
Multiparametric (mp) prostate magnetic resonance imaging (MRI) is playing an increasingly prominent role in the diagnostic work-up of patients with suspected prostate cancer. Performing mpMRI before biopsy offers several advantages including biopsy avoidance under certain clinical circumstances and targeting biopsy of suspicious lesions to enable the correct diagnosis. The success of the technique is heavily dependent on high-quality image acquisition, interpretation, and report communication, all areas addressed by previous versions of the Prostate Imaging-Reporting and Data System (PI-RADS) recommendations. Numerous studies have validated the approach, but the widespread adoption of PI-RADS version 2 has also highlighted inconsistencies and limitations, particularly relating to interobserver variability for evaluation of the transition zone. These limitations are addressed in the recently released version 2.1. In this article, we highlight the key changes proposed in PI-RADS v2.1 and explore the background reasoning and evidence for the recommendations.
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Affiliation(s)
- T Barrett
- Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Cambridge CB2 0QQ, UK.
| | - A Rajesh
- University Hospitals of Leicester NHS Trust, Leicester General Hospital, Radiology Department, Gwendolen Road, Leicester LE5 4PW, UK
| | - A B Rosenkrantz
- Department of Radiology, NYU School of Medicine, NYU Langone Medical Center, 660 1st Ave, Third Floor, New York, NY 10016, USA
| | - P L Choyke
- Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - B Turkbey
- Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA
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20
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Chatterjee A, Oto A. Future Perspectives in Multiparametric Prostate MR Imaging. Magn Reson Imaging Clin N Am 2019; 27:117-130. [DOI: 10.1016/j.mric.2018.08.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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21
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Surov A, Meyer HJ, Wienke A. Correlations between Apparent Diffusion Coefficient and Gleason Score in Prostate Cancer: A Systematic Review. Eur Urol Oncol 2019; 3:489-497. [PMID: 31412009 DOI: 10.1016/j.euo.2018.12.006] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 11/29/2018] [Accepted: 12/07/2018] [Indexed: 01/29/2023]
Abstract
BACKGROUND Reported data regarding the associations between apparent diffusion coefficient (ADC) of diffusion-weighted imaging (DWI) and Gleason score in prostate cancer (PC) are inconsistent. OBJECTIVE The aim of the present systematic review was to analyze relationships between ADC and Gleason score in PC. DESIGN, SETTING, AND PARTICIPANTS MEDLINE library, SCOPUS, and EMBASE databases were screened for relationships between ADC and Gleason score in PC up to April 2018. Overall, 39 studies with 2457 patients were identified. Data on the following parameters were extracted from the literature: number of patients, cancer localization, and correlation coefficients between ADC and Gleason score. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Associations between ADC and Gleason score were analyzed by the Spearman's correlation coefficient. RESULTS AND LIMITATIONS In overall sample, the pooled correlation coefficient between ADC and Gleason score was -0.45 (95% confidence interval [CI]=[-0.50; -0.40]). In PC in the transitional zone, the pooled correlation coefficient was -0.22 (95% CI=[-0.47; 0.03]). In PC in the peripheral zone, the pooled correlation coefficient was -0.48 (95% CI=[-0.54; -0.42]). CONCLUSIONS In PC located in the peripheral zone, ADC correlated moderately with Gleason score. In PC located in the transitional zone, ADC correlated weakly with Gleason score. PATIENT SUMMARY We reviewed studies using apparent diffusion coefficient for the prediction of Gleason score in prostate cancer patients.
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Affiliation(s)
- Alexey Surov
- Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, Germany.
| | - 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, Germany
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22
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Prospective Inclusion of Apparent Diffusion Coefficients in Multiparametric Prostate MRI Structured Reports: Discrimination of Clinically Insignificant and Significant Cancers. AJR Am J Roentgenol 2019; 212:109-116. [DOI: 10.2214/ajr.18.19937] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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23
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Alessandrino F, Taghipour M, Hassanzadeh E, Ziaei A, Vangel M, Fedorov A, Tempany CM, Fennessy FM. Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer. Abdom Radiol (NY) 2019; 44:279-285. [PMID: 30066169 PMCID: PMC6349548 DOI: 10.1007/s00261-018-1718-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PURPOSE To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard. METHODS We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3. RESULTS 119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively. CONCLUSIONS ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.
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Affiliation(s)
- Francesco Alessandrino
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA.
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
| | - Mehdi Taghipour
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Elmira Hassanzadeh
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
- Department of Radiology, University of Illinois at Chicago, Chicago, IL, USA
| | - Alireza Ziaei
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Mark Vangel
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Clare M Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
| | - Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02215, USA
- Department of Imaging, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
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Bonet‐Carne E, Johnston E, Daducci A, Jacobs JG, Freeman A, Atkinson D, Hawkes DJ, Punwani S, Alexander DC, Panagiotaki E. VERDICT-AMICO: Ultrafast fitting algorithm for non-invasive prostate microstructure characterization. NMR IN BIOMEDICINE 2019; 32:e4019. [PMID: 30378195 PMCID: PMC6492114 DOI: 10.1002/nbm.4019] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 08/30/2018] [Accepted: 09/01/2018] [Indexed: 05/10/2023]
Abstract
VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumours) estimates and maps microstructural features of cancerous tissue non-invasively using diffusion MRI. The main purpose of this study is to address the high computational time of microstructural model fitting for prostate diagnosis, while retaining utility in terms of tumour conspicuity and repeatability. In this work, we adapt the accelerated microstructure imaging via convex optimization (AMICO) framework to linearize the estimation of VERDICT parameters for the prostate gland. We compare the original non-linear fitting of VERDICT with the linear fitting, quantifying accuracy with synthetic data, and computational time and reliability (performance and precision) in eight patients. We also assess the repeatability (scan-rescan) of the parameters. Comparison of the original VERDICT fitting versus VERDICT-AMICO showed that the linearized fitting (1) is more accurate in simulation for a signal-to-noise ratio of 20 dB; (2) reduces the processing time by three orders of magnitude, from 6.55 seconds/voxel to 1.78 milliseconds/voxel; (3) estimates parameters more precisely; (4) produces similar parametric maps and (5) produces similar estimated parameters with a high Pearson correlation between implementations, r2 > 0.7. The VERDICT-AMICO estimates also show high levels of repeatability. Finally, we demonstrate that VERDICT-AMICO can estimate an extra diffusivity parameter without losing tumour conspicuity and retains the fitting advantages. VERDICT-AMICO provides microstructural maps for prostate cancer characterization in seconds.
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Affiliation(s)
- Elisenda Bonet‐Carne
- UCL Centre for Medical ImagingLondonUK
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
| | | | - Alessandro Daducci
- Computer Science DepartmentUniversity of VeronaItaly
- Radiology DepartmentCentre Hospitalier Universitaire Vaudois (CHUV)Switzerland
| | - Joseph G. Jacobs
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
| | | | | | - David J. Hawkes
- Department of Medical PhysicsUCL Centre for Medical Imaging ComputingLondonUK
| | | | - Daniel C. Alexander
- Department of Computer ScienceUCL Centre for Medical Image ComputingLondonUK
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Reproducibility of Index Lesion Size and Mean Apparent Diffusion Coefficient Values Measured by Prostate Multiparametric MRI: Correlation With Whole-Mount Sectioning of Specimens. AJR Am J Roentgenol 2018; 211:783-788. [DOI: 10.2214/ajr.17.19172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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26
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Syer TJ, Godley KC, Cameron D, Malcolm PN. The diagnostic accuracy of high b-value diffusion- and T 2-weighted imaging for the detection of prostate cancer: a meta-analysis. Abdom Radiol (NY) 2018; 43:1787-1797. [PMID: 29177924 PMCID: PMC6061488 DOI: 10.1007/s00261-017-1400-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Purpose This study aims to investigate the role of diffusion-weighted imaging (DWI) and T2-weighted imaging (T2WI) in combination for the detection of prostate cancer, specifically assessing the role of high b-values (> 1000 s/mm2), with a systematic review and meta-analysis of the existing published data. Methods The electronic databases MEDLINE, EMBASE, and OpenSIGLE were searched between inception and September 1, 2017. Eligible studies were those that reported the sensitivity and specificity of DWI and T2WI for the diagnosis of prostate cancer by visual assessment using a histopathologic reference standard. The QUADAS-2 critical appraisal tool was used to assess the quality of included studies. A meta-analysis with pooling of sensitivity, specificity, likelihood, and diagnostic odds ratios was undertaken, and a summary receiver-operating characteristics (sROC) curve was constructed. Predetermined subgroup analysis was also performed. Results Thirty-three studies were included in the final analysis, evaluating 2949 patients. The pooled sensitivity and specificity were 0.69 (95% CI 0.68–0.69) and 0.84 (95% CI 0.83–0.85), respectively, and the sROC AUC was 0.84 (95% CI 0.81–0.87). Subgroup analysis showed significantly better sensitivity with high b-values (> 1000 s/mm2). There was high statistical heterogeneity between studies. Conclusion The diagnostic accuracy of combined DWI and T2WI is good with high b-values (> 1000 s/mm2) seeming to improve overall sensitivity while maintaining specificity. However, further large-scale studies specifically looking at b-value choice are required before a categorical recommendation can be made. Electronic supplementary material The online version of this article (10.1007/s00261-017-1400-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tom J. Syer
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ UK
| | - Keith C. Godley
- Radiology Department, Norfolk & Norwich University NHS Foundation Trust, Colney Lane, Norfolk Norwich, NR4 7UY UK
| | - Donnie Cameron
- Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, Norfolk NR4 7TJ UK
| | - Paul N. Malcolm
- Radiology Department, Norfolk & Norwich University NHS Foundation Trust, Colney Lane, Norfolk Norwich, NR4 7UY UK
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New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer? Abdom Radiol (NY) 2018; 43:702-712. [PMID: 28721479 DOI: 10.1007/s00261-017-1255-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria. METHODS This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system. RESULTS The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%. CONCLUSIONS MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.
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Multiparametric magnetic resonance imaging for transition zone prostate cancer: essential findings, limitations, and future directions. Abdom Radiol (NY) 2017; 42:2732-2744. [PMID: 28702787 DOI: 10.1007/s00261-017-1184-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Review the multiparametric MRI (mpMRI) findings of transition zone (TZ) prostate cancer (PCa) using T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI and to integrate mpMRI findings with clinical history, laboratory values, and histopathology. CONCLUSION TZ prostate tumors are challenging to detect clinically and at MRI. mpMRI using the combination of sequences has the potential to improve accuracy of TZ cancer detection and staging.
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Hurrell SL, McGarry SD, Kaczmarowski A, Iczkowski KA, Jacobsohn K, Hohenwalter MD, Hall WA, See WA, Banerjee A, Charles DK, Nevalainen MT, Mackinnon AC, LaViolette PS. Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging. J Med Imaging (Bellingham) 2017; 5:011004. [PMID: 29098169 PMCID: PMC5658575 DOI: 10.1117/1.jmi.5.1.011004] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 09/21/2017] [Indexed: 01/21/2023] Open
Abstract
Multiparametric magnetic resonance imaging (MP-MRI), including diffusion-weighted imaging, is commonly used to diagnose prostate cancer. This radiology–pathology study correlates prostate cancer grade and morphology with common b-value combinations for calculating apparent diffusion coefficient (ADC). Thirty-nine patients undergoing radical prostatectomy were recruited for MP-MRI prior to surgery. Diffusion imaging was collected with seven b-values, and ADC was calculated. Excised prostates were sliced in the same orientation as the MRI using 3-D printed slicing jigs. Whole-mount slides were digitized and annotated by a pathologist. Annotated samples were aligned to the MRI, and ADC values were extracted from annotated peripheral zone (PZ) regions. A receiver operating characteristic (ROC) analysis was performed to determine accuracy of tissue type discrimination and optimal ADC b-value combination. ADC significantly discriminates Gleason (G) G4-5 cancer from G3 and other prostate tissue types. The optimal b-values for discriminating high from low-grade and noncancerous tissue in the PZ are 50 and 2000, followed closely by 100 to 2000 and 0 to 2000. Optimal ADC cut-offs are presented for dichotomized discrimination of tissue types according to each b-value combination. Selection of b-values affects the sensitivity and specificity of ADC for discrimination of prostate cancer.
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Affiliation(s)
- Sarah L Hurrell
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Sean D McGarry
- Medical College of Wisconsin, Department of Biophysics, Milwaukee, Wisconsin, United States
| | - Amy Kaczmarowski
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - Kenneth A Iczkowski
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Kenneth Jacobsohn
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Mark D Hohenwalter
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States
| | - William A Hall
- Medical College of Wisconsin, Department of Radiation Oncology, Milwaukee, Wisconsin, United States
| | - William A See
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Anjishnu Banerjee
- Medical College of Wisconsin, Department of Biostatistics, Milwaukee, Wisconsin, United States
| | - David K Charles
- Medical College of Wisconsin, Department of Urology, Milwaukee, Wisconsin, United States
| | - Marja T Nevalainen
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Pharmacology and Toxicology, Milwaukee, Wisconsin, United States
| | - Alexander C Mackinnon
- Medical College of Wisconsin, Department of Pathology, Milwaukee, Wisconsin, United States
| | - Peter S LaViolette
- Medical College of Wisconsin, Department of Radiology, Milwaukee, Wisconsin, United States.,Medical College of Wisconsin, Department of Biomedical Engineering, Milwaukee, Wisconsin, United States
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Adubeiro N, Nogueira ML, Nunes RG, Ferreira HA, Ribeiro E, La Fuente JMF. Apparent diffusion coefficient in the analysis of prostate cancer: determination of optimal b-value pair to differentiate normal from malignant tissue. Clin Imaging 2017; 47:90-95. [PMID: 28917137 DOI: 10.1016/j.clinimag.2017.09.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 09/04/2017] [Accepted: 09/06/2017] [Indexed: 12/16/2022]
Abstract
PURPOSE Determining optimal b-value pair for differentiation between normal and prostate cancer (PCa) tissues. METHODS Forty-three patients with diagnosis or PCa symptoms were included. Apparent diffusion coefficient (ADC) was estimated using minimum and maximum b-values of 0, 50, 100, 150, 200, 500s/mm2 and 500, 800, 1100, 1400, 1700 and 2000s/mm2, respectively. Diagnostic performances were evaluated when Area-under-the-curve (AUC)>95%. RESULTS 15 of the 35 b-values pair surpassed this AUC threshold. The pair (50, 2000s/mm2) provided the highest AUC (96%) with ADC cutoff 0.89×10-3mm2/s, sensitivity 95.5%, specificity 93.2% and accuracy 94.4%. CONCLUSIONS The best b-value pair was b=50, 2000s/mm2.
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Affiliation(s)
- Nuno Adubeiro
- Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal; Department of Radiology, School of Health of Porto/Polytechnic Institute of Porto (ESS/IPP), Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal.
| | - Maria Luísa Nogueira
- Department of Radiology, School of Health of Porto/Polytechnic Institute of Porto (ESS/IPP), Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal
| | - Rita G Nunes
- Institute for Systems and Robotics and Department of Bioengineering, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
| | - Hugo Alexandre Ferreira
- Institute of Biophysics and Biomedical Engineering (IBEB), Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016 Lisboa, Portugal
| | - Eduardo Ribeiro
- Department of Radiology, MRI Unit, Centro Hospitalar do Porto, Largo Prof. Abel Salazar, 4099-001 Porto, Portugal; Department of Radiology, School of Health of Porto (ESS), Rua Dr. António Bernardino de Almeida 400, 4200-072 Porto, Portugal
| | - José Maria Ferreira La Fuente
- Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal.; Department of Urology, Center Hospitalar Porto (CHP), Largo Prof. Abel Salazar, 4099-001 Porto, Portugal
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Skorpil M, Brynolfsson P, Engström M. Motion corrected DWI with integrated T2-mapping for simultaneous estimation of ADC, T2-relaxation and perfusion in prostate cancer. Magn Reson Imaging 2017; 39:162-167. [DOI: 10.1016/j.mri.2017.03.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 03/08/2017] [Accepted: 03/08/2017] [Indexed: 01/05/2023]
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Purysko AS, Rosenkrantz AB, Barentsz JO, Weinreb JC, Macura KJ. PI-RADS Version 2: A Pictorial Update. Radiographics 2016; 36:1354-72. [DOI: 10.1148/rg.2016150234] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study. AJR Am J Roentgenol 2016; 206:559-65. [PMID: 26901012 DOI: 10.2214/ajr.15.14967] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The objective of our study was to investigate associations between quantitative image features of multiparametric MRI of the prostate and PTEN expression of peripheral zone prostate cancer. MATERIALS AND METHODS A total of 45 peripheral zone cancer foci from 30 patients who had undergone multiparametric prostate MRI before prostatectomy were identified by a genitourinary pathologist and a radiologist who reviewed histologic findings and MR images. Histologic sections of cancer foci underwent immunohistochemical analysis and were scored according to the percentage of tumor-positive cells expressing PTEN as negative (0-20%), mixed (20-80%), or positive (80-100%). Average and 10th percentile apparent diffusion coefficient (ADC) values, skewness of T2-weighted signal intensity histogram, and quantitative perfusion parameters (i.e., forward volume transfer constant [K(trans)], extravascular extracellular volume fraction [ve], and reverse reflux rate constant between the extracellular space and plasma [k(ep)]) from the Tofts model were calculated for each cancer focus. Associations between the quantitative image features and PTEN expression were analyzed with the Spearman rank correlation coefficient (r). RESULTS Analysis of the 45 cancer foci revealed that 21 (47%) were PTEN-positive, 12 (27%) were PTEN-negative, and 12 (27%) were mixed. There was a weak but significant negative correlation between Gleason score and PTEN expression (r = -0.30, p = 0.04) and between k(ep) and PTEN expression (r = -0.35, p = 0.02). There was no significant correlation between other multiparametric MRI features and PTEN expression. CONCLUSION This preliminary study of radiogenomics of peripheral zone prostate cancer revealed weak-but significant-associations between the quantitative dynamic contrast-enhanced MRI feature k(ep) and Gleason score with PTEN expression. These findings warrant further investigation and validation with the aim of using multiparametric MRI to improve risk assessment of patients with prostate cancer.
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New RESOLVE-Based Diffusional Kurtosis Imaging in MRI-Visible Prostate Cancer: Effect of Reduced b Value on Image Quality and Diagnostic Effectiveness. AJR Am J Roentgenol 2016; 207:330-8. [PMID: 27187062 DOI: 10.2214/ajr.15.15990] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
OBJECTIVE The purpose of this article was to investigate whether a new readout segmentation of long variable echo-trains (RESOLVE)-based diffusional kurtosis imaging (DKI) with reduced b value technique can affect image quality and diagnostic effectiveness in MRI-visible prostate cancer (PCA). SUBJECTS AND METHODS Prostatic RESOLVE DKI (0-1400 s/mm2) was prospectively performed for 12 volunteers. The optimal protocol was then performed in 108 MRI-visible PCAs to determine whether it can compete against a preferred b-value set (0-2000 s/mm(2)) regarding image quality and diagnostic effectiveness. Images were interpreted by two independent radiologists using the prostate imaging reporting and data system (PI-RADS). Readers' concordance and diagnostic effectiveness were tested with the Fleiss kappa and area under the ROC curve (Az) analyses. RESULTS A b value of 1400 s/mm(2) generated a larger apparent diffusion coefficient of gaussian distribution (Dapp) (1.35 ± 0.31 vs 1.30 ± 0.30 mm(2)/s; p < 0.001) and apparent kurtosis coefficient (Kapp) (1.11 ± 0.26 vs 1.00 ± 0.21; p < 0.001) in PCA than did a b value of 2000 s/mm(2). Interreader agreement using PI-RADS was relatively low when Dapp and Kapp maps were excluded from image interpretations (κ = 0.39-0.41 vs κ = 0.66-0.68 with Dapp and Kapp maps). Interreader agreement in staging PCA was relatively high (κ > 0.80) and was not influenced by reducing the b value. The power of Dapp and Kapp to differentiate PCA from normal tissue (Az = 0.97-0.98), tissue with a Gleason score less than or equal to 3 + 4 from tissue with a Gleason score greater than 3 + 4 (Az = 0.77-0.82), and PCA stage lower than pT3 from stage pT3 and higher PCA (Az = 0.70-0.75) was not significantly degraded by reducing the b value. CONCLUSION We found that b values significantly influenced image quality, PI-RADS score, and DKI outputs but did not degrade the diagnostic effectiveness of DKI parameters to detect and classify PCA.
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Abstract
The added value of diffusion-weighted magnetic resonance imaging (DW-MRI) for the detection, localization, and staging of primary prostate cancer has been extensively reported in original studies and meta-analyses. More recently, DW-MRI and related techniques have been used to noninvasively assess prostate cancer aggressiveness and estimate its biological behavior. The present article aims to summarize the potential applications of DW-MRI for noninvasive optimization of pretherapeutic risk assessment, patient management decisions, and evaluation of treatment response.
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de Perrot T, Scheffler M, Boto J, Delattre BMA, Combescure C, Pusztaszeri M, Tille JC, Iselin C, Vallée JP. Diffusion in prostate cancer detection on a 3T scanner: How many b-values are needed? J Magn Reson Imaging 2016; 44:601-9. [DOI: 10.1002/jmri.25206] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2015] [Accepted: 02/09/2016] [Indexed: 12/30/2022] Open
Affiliation(s)
- Thomas de Perrot
- Division of Radiology; Geneva University Hospitals; Geneva Switzerland
| | - Max Scheffler
- Division of Radiology; Geneva University Hospitals; Geneva Switzerland
| | - José Boto
- Division of Radiology; Geneva University Hospitals; Geneva Switzerland
| | | | | | - Marc Pusztaszeri
- Division of Clinical Pathology; Geneva University Hospitals; Geneva Switzerland
| | | | - Christophe Iselin
- Division of Urologic Surgery; Geneva University Hospitals; Geneva Switzerland
| | - Jean-Paul Vallée
- Division of Radiology; Geneva University Hospitals; Geneva Switzerland
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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: 40] [Impact Index Per Article: 5.0] [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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Hernández-Argüello M, Quiceno H, Pascual I, Solorzano JL, Benito A, Collantes M, Rodríguez-Fraile M, Pardo J, Richter JA. Index lesion characterization by (11)C-Choline PET/CT and Apparent Diffusion Coefficient parameters at 3 Tesla MRI in primary prostate carcinoma. Prostate 2016; 76:3-12. [PMID: 26390847 DOI: 10.1002/pros.23038] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2015] [Accepted: 05/21/2015] [Indexed: 01/18/2023]
Abstract
BACKGROUND Index lesion characterization is important in the evaluation of primary prostate carcinoma (PPC). The aim of this study was to analyze the contribution of (11) C-Choline PET/CT and the Apparent Diffusion Coefficient maps (ADC) in detecting the Index Lesion and clinically significant tumors in PPC. METHODS Twenty-one untreated patients with biopsy-proven PPC and candidates for radical prostatectomy (RP) were prospectively evaluated by means of Ultra-High Definition PET/CT and 3T MRI, which included T2-weighted imaging (T2WI) and ADC maps obtained from diffusion weighted imaging (DWI). Independent experts analyzed all the images separately and were unaware of the pathological data. In each case, the Index lesion was defined as the largest tumor measured on histopathology (Index H). In addition, the largest lesion observed on MRI (Index MRI) and the highest avid (11) C-Choline uptake lesion (Index PET) were obtained. The Gleason scores (GS) of the tumors were determined. PET/CT and ADC map quantitative parameters were also calculated. Measures of correlation among imaging parameters as well as the sensitivity (S), specificity (Sp), negative and positive predictive values (NPV and PPV) for tumor detection were analyzed. All data was validated with the pathological study. RESULTS In the morphological study, 139 foci of carcinoma were identified, 47 of which corresponded to clinically significant tumors (>0.5 cm(3)). The remaining foci presented a maximum diameter (dmax ) of 0.1 cm ± SD 0.75 and were not classified as clinically significant. Thirty-two tumors presented a GS (3 + 3), nine GS (3 + 4), and six GS (4 + 3). A total of 21 Index H (dmax = 1.37 cm SD ± 0.61) were identified. The S, Sp, NPV, and PPV for tumor detection with PET were 100%, 70%, 83%, 100%, and for MRI were 46%, 100%, 100%, 54%, respectively. Both Index PET and Index MRI were complementary and identified 95% of the Index H when quantitative criteria were used. CONCLUSION In spite of the fact that PET imaging has higher tumor sensitivity than MRI, (11) C-Choline PET and ADC maps have complementary roles in the evaluation of Index Lesion in PPC. Index PET and Index MRI could be complementary targets in the therapeutic planning of PPC.
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Affiliation(s)
| | - Hernán Quiceno
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Ignacio Pascual
- Department of Urology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - José L Solorzano
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Alberto Benito
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
| | - María Collantes
- Department of Nuclear Medicine, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Macarena Rodríguez-Fraile
- Department of Nuclear Medicine, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - Javier Pardo
- Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
| | - José A Richter
- Department of Nuclear Medicine, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, Pamplona, Spain
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Barrett T, Priest AN, Lawrence EM, Goldman DA, Warren AY, Gnanapragasam VJ, Sala E, Gallagher FA. Ratio of Tumor to Normal Prostate Tissue Apparent Diffusion Coefficient as a Method for Quantifying DWI of the Prostate. AJR Am J Roentgenol 2015; 205:W585-93. [PMID: 26587948 DOI: 10.2214/ajr.15.14338] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2024]
Abstract
OBJECTIVE The purpose of this study was to investigate the ability of the apparent diffusion coefficient (ADC) ratio of tumor to normal prostate tissue to overcome inherent variability based on choice of b values, with whole-mount histopathologic analysis as the reference standard for tumor identification. MATERIALS AND METHODS Thirty-nine patients with prostate cancer underwent 3-T MRI, including DWI with b values of 0, 150, 750, and 1000 s/mm(2). ADC maps were derived from four b value combinations. Histologically derived ROIs were defined for prostate tumor and benign prostate tissue to generate a ratio. The concordance correlation coefficient was used to evaluate agreement and reproducibility at different b values. Bland-Altman plots were used to evaluate the pattern of relative measurement difference between b value combinations. The relationship between ADC values and Gleason score was tested by Spearman rank correlation. RESULTS ADC values varied depending on the b value combination selected. The concordance correlation coefficient was higher for ADC ratios (0.883; 95% CI, 0.816-0.927) compared with absolute ADC values for normal tissue (0.873; 95% CI, 0.799-0.921) and tumor (0.792; 95% CI, 0.688-0.864). The ADC ratio concordance correlation coefficient for transition zone tumors was considerably higher than that for the peripheral zone in all cases. Bland-Altman analysis showed higher variation for ADC maps incorporating a b value of zero for both ratio and absolute values. There was a stronger inverse relationship to Gleason score for ADC ratios (rho, -0.354 to -0.456) compared with absolute ADC values (rho, -0.117 to -0.379). CONCLUSION The use of a simple ratio of prostate tumor ADC to normal tissue ADC improved the concordance between different b value combinations and could provide a more robust means of assessing restricted diffusion in the prostate.
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Affiliation(s)
- Tristan Barrett
- 1 Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, UK
| | - Andrew N Priest
- 1 Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, UK
| | - Edward M Lawrence
- 1 Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, UK
| | - Debra A Goldman
- 2 Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Anne Y Warren
- 3 Department of Histopathology, Addenbrooke's Hospital and the University of Cambridge, Cambridge, United Kingdom
| | - Vincent J Gnanapragasam
- 4 Department of Urology, Addenbrooke's Hospital, Cambridge, United Kingdom
- 5 Department of Oncology, Translational Prostate Cancer Group, University of Cambridge, Hutchinson-MRC Research Centre, Cambridge, United Kingdom
| | - Evis Sala
- 1 Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, UK
- 6 Present address: Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Ferdia A Gallagher
- 1 Department of Radiology, Addenbrooke's Hospital and the University of Cambridge, Hills Rd, Cambridge, CB2 0QQ, UK
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Karki K, Hugo GD, Ford JC, Olsen KM, Saraiya S, Groves R, Weiss E. Estimation of optimal b-value sets for obtaining apparent diffusion coefficient free from perfusion in non-small cell lung cancer. Phys Med Biol 2015; 60:7877-91. [PMID: 26406921 DOI: 10.1088/0031-9155/60/20/7877] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The purpose of this study was to determine optimal sets of b-values in diffusion-weighted MRI (DW-MRI) for obtaining monoexponential apparent diffusion coefficient (ADC) close to perfusion-insensitive intravoxel incoherent motion (IVIM) model ADC (ADCIVIM) in non-small cell lung cancer. Ten subjects had 40 DW-MRI scans before and during radiotherapy in a 1.5 T MRI scanner. Respiratory triggering was applied to the echo-planar DW-MRI with TR ≈ 4500 ms, TE = 74 ms, eight b-values of 0-1000 μs μm(-2), pixel size = 1.98 × 1.98 mm(2), slice thickness = 6 mm, interslice gap = 1.2 mm, 7 axial slices and total acquisition time ≈6 min. One or more DW-MRI scans together covered the whole tumour volume. Monoexponential model ADC values using various b-value sets were compared to reference-standard ADCIVIM values using all eight b-values. Intra-scan coefficient of variation (CV) of active tumour volumes was computed to compare the relative noise in ADC maps. ADC values for one pre-treatment DW-MRI scan of each of the 10 subjects were computed using b-value pairs from DW-MRI images synthesized for b-values of 0-2000 μs μm(-2) from the estimated IVIM parametric maps and corrupted by various Rician noise levels. The square root of mean of squared error percentage (RMSE) of the ADC value relative to the corresponding ADCIVIM for the tumour volume of the scan was computed. Monoexponential ADC values for the b-value sets of 250 and 1000; 250, 500 and 1000; 250, 650 and 1000; 250, 800 and 1000; and 250-1000 μs μm(-2) were not significantly different from ADCIVIM values (p > 0.05, paired t-test). Mean error in ADC values for these sets relative to ADCIVIM were within 3.5%. Intra-scan CVs for these sets were comparable to that for ADCIVIM. The monoexponential ADC values for other sets-0-1000; 50-1000; 100-1000; 500-1000; and 250 and 800 μs μm(-2) were significantly different from the ADCIVIM values. From Rician noise simulation using b-value pairs, there was a wide range of acceptable b-value pairs giving small RMSE of ADC values relative to ADCIVIM. The pairs for small RMSE had lower b-values as the noise level increased. ADC values of a two b-value set-250 and 1000 μs μm(-2), and all three b-value sets with 250, 1000 μs μm(-2) and an intermediate value approached ADCIVIM, with relative noise comparable to that of ADCIVIM. These sets may be used in lung tumours using comparatively short scan and post-processing times. Rician noise simulation suggested that the b-values in the vicinity of these experimental best b-values can be used with error within an acceptable limit. It also suggested that the optimal sets will have lower b-values as the noise level becomes higher.
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Affiliation(s)
- Kishor Karki
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23284, USA
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Merisaari H, Toivonen J, Pesola M, Taimen P, Boström PJ, Pahikkala T, Aronen HJ, Jambor I. Diffusion-weighted imaging of prostate cancer: effect of b-value distribution on repeatability and cancer characterization. Magn Reson Imaging 2015. [PMID: 26220861 DOI: 10.1016/j.mri.2015.07.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
PURPOSE To evaluate the effect of b-value distribution on the repeatability and Gleason score (GS) prediction of prostate cancer (PCa). METHODS Fifty PCa patients underwent two repeated 3T diffusion-weighted imaging (DWI) examinations using 12 b values in the range from 0 to 2000s/mm(2) and diffusion time of 20.3ms. Mean signal intensities of regions of interest, placed in PCa using whole mount prostatectomy sections as the reference, were fitted using monoexponential, kurtosis, stretched exponential, and biexponential models. In total, 4083 different b-value combinations consisting of 2 to 12 b values were evaluated. Repeatability was assessed by intraclass correlation coefficient, ICC(3,1), and coefficient of repeatability (CoR). Areas under receiver operating characteristic curve (AUCs) for PCa characterization were estimated while the correlation of the fitted values with GS groups (3+3, 3+4, >3+4) was evaluated by using the Spearman correlation coefficient (ρ). RESULTS The parameters of monoexponential, kurtosis, and stretched exponential models estimated using only 4-5, 5-7, 5-7 b values, respectively, had similar ICC(3,1), CoR, AUC, and ρ values as the parameters estimated using all 12 b values. Optimized b-value distributions demonstrated improved ICC(3,1) and CoR values but failed to improve AUC and ρ values. The parameters of biexponential model demonstrated the worst repeatability and diagnostic performance. CONCLUSION B-value distribution influences mainly the repeatability of DWI-derived parameters rather than the diagnostic performance.
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Affiliation(s)
- Harri Merisaari
- Department of Information Technology, University of Turku, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland
| | - Jussi Toivonen
- Department of Information Technology, University of Turku, Turku, Finland; Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Marko Pesola
- Department of Diagnostic Radiology, University of Turku, 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
| | - 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.
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Jambor I, Pesola M, Merisaari H, Taimen P, Boström PJ, Liimatainen T, Aronen HJ. Relaxation along fictitious field, diffusion-weighted imaging, and T2 mapping of prostate cancer: Prediction of cancer aggressiveness. Magn Reson Med 2015; 75:2130-40. [PMID: 26094849 DOI: 10.1002/mrm.25808] [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: 03/05/2015] [Revised: 05/20/2015] [Accepted: 05/21/2015] [Indexed: 12/13/2022]
Abstract
PURPOSE To evaluate the performance of relaxation along a fictitious field (RAFF) relaxation time (TRAFF ), diffusion-weighted imaging (DWI)-derived parameters, and T2 relaxation time values for prostate cancer (PCa) detection and characterization. METHODS Fifty patients underwent 3T MR examination using surface array coils before prostatectomy. DWI was performed using 14 and 12 b values in the ranges of 0-500 s/mm(2) and 0-2000 s/mm(2) , respectively. Repeated MR examination was performed in 16 patients. TRAFF , DWI-derived parameters (monoexponential, kurtosis, biexponential models), and T2 values were measured and averaged over regions of interest placed in PCa and normal tissue. Repeatability of TRAFF and DWI-derived parameters were assessed by coefficient of repeatability and intraclass correlation coefficient ICC(3,1). Areas under the receiver operating characteristic curve (AUCs) for PCa detection and Gleason score classification were estimated. The parameters were correlated with Gleason score groups using Spearman correlation coefficient (ρ). RESULTS ICC(3,1) values for TRAFF were in the range of 0.82-0.92. TRAFF values had higher AUC values for Gleason score classification compared with DWI-derived parameters and T2 . The RAFF method demonstrated the highest ρ value (-0.65). CONCLUSION In a quantitative region of interest-based analysis, RAFF outperformed DWI ("low" and "high" b values) and T2 mapping in the characterization of PCa.
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Affiliation(s)
- Ivan Jambor
- Department of Radiology, University of Turku, Turku, Finland
| | - Marko Pesola
- Department of Radiology, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Information Technology, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland
| | - Pekka Taimen
- Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Timo Liimatainen
- Department of Biotechnology and Molecular Medicine, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.,Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Hannu J Aronen
- Department of Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
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Loch R, Fowler K, Schmidt R, Ippolito J, Siegel C, Narra V. Prostate Magnetic Resonance Imaging: Challenges of Implementation. Curr Probl Diagn Radiol 2015; 44:26-37. [DOI: 10.1067/j.cpradiol.2014.05.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Revised: 05/20/2014] [Accepted: 05/21/2014] [Indexed: 01/13/2023]
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High-b-value diffusion-weighted MRI for the detection of prostate cancer at 3 T. Clin Radiol 2014; 69:1165-70. [DOI: 10.1016/j.crad.2014.07.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2014] [Revised: 07/11/2014] [Accepted: 07/16/2014] [Indexed: 01/08/2023]
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45
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Merisaari H, Jambor I. Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm(2): simulation and repeatability study. Magn Reson Med 2014; 73:1954-69. [PMID: 25045885 DOI: 10.1002/mrm.25310] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 05/12/2014] [Accepted: 05/15/2014] [Indexed: 12/20/2022]
Abstract
PURPOSE To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations. METHODS Simulations aiming to minimize estimation accuracy error were performed. Ten PCa patients underwent in total four repeated 3-tesla DWI examinations using 12 equally spaced b values (0-2000 s/mm(2) ). Normalized mean signal intensities of regions-of-interest placed in normal tissue and PCa were fitted. In total, 210 different b-value combinations consisting of six b values, 0 and 100 s/mm(2) included in every b-value distribution, were evaluated in terms of accuracy and repeatability. RESULTS The simulations and in vivo DWI data suggest the optimal b-value distribution for the monoexponential model consists of four to five equally distributed b values in the range of 0 to 1200 s/mm(2) . The parameters of the stretched exponential and kurtosis models are best estimated using five to seven b values in the ranges of 300 to 700 and close to 2000 s/mm(2) , in addition to low b value. B-value distribution consisting of eight to 10 b values in the ranges of 0 to 100, 800 to 1200, and 1800 to 2000 s/mm(2) is the preferred method for estimation of the biexponential model parameters of PCa DWI. CONCLUSION The optimized b-value distributions demonstrated improved estimation accuracy and repeatability of DWI signal decay-derived parameters.
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Affiliation(s)
- Harri Merisaari
- Department of Information Technology, University of Turku, Turku, Finland; Turku PET Centre, University of Turku, Turku, Finland
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