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Agrotis G, Pooch E, Abdelatty M, Benson S, Vassiou A, Vlychou M, Beets-Tan RGH, Schoots IG. Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10890-6. [PMID: 38995382 DOI: 10.1007/s00330-024-10890-6] [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: 02/18/2024] [Revised: 04/26/2024] [Accepted: 05/19/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVES To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection. MATERIALS AND METHODS A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated. RESULTS Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively). CONCLUSION Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers. CLINICAL RELEVANCE STATEMENT In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments. KEY POINTS MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.
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Affiliation(s)
- Georgios Agrotis
- Department of Radiology, University Hospital of Larissa, Larissa, Greece.
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Eduardo Pooch
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Mohamed Abdelatty
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Diagnostic and Interventional Radiology, Kasr Al-Ainy Hospital, Cairo University, Giza, Egypt
| | - Sean Benson
- Department of Diagnostic and Interventional Radiology, Kasr Al-Ainy Hospital, Cairo University, Giza, Egypt
| | - Aikaterini Vassiou
- Department of Radiology, University Hospital of Larissa, Larissa, Greece
| | - Marianna Vlychou
- Department of Radiology, University Hospital of Larissa, Larissa, Greece
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Ivo G Schoots
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology & Nuclear Medicine, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands
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2
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McTavish S, Van AT, Peeters JM, Weiss K, Harder FN, Makowski MR, Braren RF, Karampinos DC. Partial Fourier in the presence of respiratory motion in prostate diffusion-weighted echo planar imaging. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01162-x. [PMID: 38743376 DOI: 10.1007/s10334-024-01162-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/05/2024] [Accepted: 04/24/2024] [Indexed: 05/16/2024]
Abstract
PURPOSE To investigate the effect of respiratory motion in terms of signal loss in prostate diffusion-weighted imaging (DWI), and to evaluate the usage of partial Fourier in a free-breathing protocol in a clinically relevant b-value range using both single-shot and multi-shot acquisitions. METHODS A controlled breathing DWI acquisition was first employed at 3 T to measure signal loss from deep breathing patterns. Single-shot and multi-shot (2-shot) acquisitions without partial Fourier (no pF) and with partial Fourier (pF) factors of 0.75 and 0.65 were employed in a free-breathing protocol. The apparent SNR and ADC values were evaluated in 10 healthy subjects to measure if low pF factors caused low apparent SNR or overestimated ADC. RESULTS Controlled breathing experiments showed a difference in signal coefficient of variation between shallow and deep breathing. In free-breathing single-shot acquisitions, the pF 0.65 scan showed a significantly (p < 0.05) higher apparent SNR than pF 0.75 and no pF in the peripheral zone (PZ) of the prostate. In the multi-shot acquisitions in the PZ, pF 0.75 had a significantly higher apparent SNR than 0.65 pF and no pF. The single-shot pF 0.65 scan had a significantly lower ADC than single-shot no pF. CONCLUSION Deep breathing patterns can cause intravoxel dephasing in prostate DWI. For single-shot acquisitions at a b-value of 800 s/mm2, any potential risks of motion-related artefacts at low pF factors (pF 0.65) were outweighed by the increase in signal from a lower TE, as shown by the increase in apparent SNR. In multi-shot acquisitions however, the minimum pF factor should be larger, as shown by the lower apparent SNR at low pF factors.
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Affiliation(s)
- Sean McTavish
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany.
| | - Anh T Van
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | | | | | - Felix N Harder
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Rickmer F Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, Munich, Germany
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3
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Lorenzo G, Heiselman JS, Liss MA, Miga MI, Gomez H, Yankeelov TE, Reali A, Hughes TJ. A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model. CANCER RESEARCH COMMUNICATIONS 2024; 4:617-633. [PMID: 38426815 PMCID: PMC10906139 DOI: 10.1158/2767-9764.crc-23-0449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/15/2023] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer. SIGNIFICANCE Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.
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Affiliation(s)
- Guillermo Lorenzo
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
| | - Jon S. Heiselman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Michael A. Liss
- Department of Urology, The University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - Michael I. Miga
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee
- Vanderbilt Institute for Surgery and Engineering, Vanderbilt University, Nashville, Tennessee
- Department of Neurological Surgery, Radiology, and Otolaryngology-Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Gomez
- School of Mechanical Engineering, Weldon School of Biomedical Engineering, and Purdue Institute for Cancer Research, Purdue University, West Lafayette, Indiana
| | - Thomas E. Yankeelov
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
- Livestrong Cancer Institutes and Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, The University of Texas at Austin, Austin, Texas
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alessandro Reali
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Thomas J.R. Hughes
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas
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4
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Zhang Z, Aygun E, Shih SF, Raman SS, Sung K, Wu HH. High-resolution prostate diffusion MRI using eddy current-nulled convex optimized diffusion encoding and random matrix theory-based denoising. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01147-w. [PMID: 38349453 DOI: 10.1007/s10334-024-01147-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 12/12/2023] [Accepted: 01/04/2024] [Indexed: 02/17/2024]
Abstract
OBJECTIVE To develop and evaluate a technique combining eddy current-nulled convex optimized diffusion encoding (ENCODE) with random matrix theory (RMT)-based denoising to accelerate and improve the apparent signal-to-noise ratio (aSNR) and apparent diffusion coefficient (ADC) mapping in high-resolution prostate diffusion-weighted MRI (DWI). MATERIALS AND METHODS: Eleven subjects with clinical suspicion of prostate cancer were scanned at 3T with high-resolution (HR) (in-plane: 1.0 × 1.0 mm2) ENCODE and standard-resolution (1.6 × 2.2 mm2) bipolar DWI sequences (both had 7 repetitions for averaging, acquisition time [TA] of 5 min 50 s). HR-ENCODE was retrospectively analyzed using three repetitions (accelerated effective TA of 2 min 30 s). The RMT-based denoising pipeline utilized complex DWI signals and Marchenko-Pastur distribution-based principal component analysis to remove additive Gaussian noise in images from multiple coils, b-values, diffusion encoding directions, and repetitions. HR-ENCODE with RMT-based denoising (HR-ENCODE-RMT) was compared with HR-ENCODE in terms of aSNR in prostate peripheral zone (PZ) and transition zone (TZ). Precision and accuracy of ADC were evaluated by the coefficient of variation (CoV) between repeated measurements and mean difference (MD) compared to the bipolar ADC reference, respectively. Differences were compared using two-sided Wilcoxon signed-rank tests (P < 0.05 considered significant). RESULTS HR-ENCODE-RMT yielded 62% and 56% higher median aSNR than HR-ENCODE (b = 800 s/mm2) in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT achieved 63% and 70% lower ADC-CoV than HR-ENCODE in PZ and TZ, respectively (P < 0.001). HR-ENCODE-RMT ADC and bipolar ADC had low MD of 22.7 × 10-6 mm2/s in PZ and low MD of 90.5 × 10-6 mm2/s in TZ. CONCLUSIONS HR-ENCODE-RMT can shorten the acquisition time and improve the aSNR of high-resolution prostate DWI and achieve accurate and precise ADC measurements in the prostate.
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Affiliation(s)
- Zhaohuan Zhang
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Elif Aygun
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven S Raman
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
| | - Kyunghyun Sung
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA, 90095, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
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5
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Deforche M, Lefebvre Y, Diamand R, Bali MA, Lemort M, Coquelet N. Improved diagnostic accuracy of readout-segmented echo-planar imaging for peripheral zone clinically significant prostate cancer: a retrospective 3T MRI study. Sci Rep 2024; 14:3299. [PMID: 38332131 PMCID: PMC10853221 DOI: 10.1038/s41598-024-53898-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024] Open
Abstract
This study compares the readout-segmented echo-planar imaging (rsEPI) from the conventional single-shot EPI (ssEPI) diffusion-weighted imaging (DWI) for the discrimination of patients with clinically significant prostate cancer (csPCa) within the peripheral zone (PZ) using apparent diffusion coefficient (ADC) maps and pathology report from magnetic resonance imaging (MRI)-targeted biopsy. We queried a retrospective monocentric database of patients with targeted biopsy. csPCa patients were defined as an International Society of Urological Pathology grade group ≥ 2. Group-level analyses and diagnostic accuracy of mean ADC values (ADCmean) within the tumor volume were assessed from Kruskal-Wallis tests and receiving operating characteristic curves, respectively. Areas under the curve (AUC) and optimal cut-off values were calculated. 159 patients (105 rsEPI, 54 ssEPI; mean age ± standard deviation: 65 ± 8 years) with 3T DWI, PZ lesions and targeted biopsy were selected. Both DWI sequences showed significantly lower ADCmean values for patients with csPCa. The rsEPI sequence better discriminates patients with csPCa (AUCrsEPI = 0.84, AUCssEPI = 0.68, p < 0.05) with an optimal cut-off value of 1232 μm2/s associated with a sensitivity-specificity of 97%-63%. Our study showed that the rsEPI DWI sequence enhances the discrimination of patients with csPCa.
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Affiliation(s)
- M Deforche
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - Y Lefebvre
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - R Diamand
- Urology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, Brussels, Belgium
| | - M A Bali
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - M Lemort
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium
| | - N Coquelet
- Radiology Department, Institut Jules Bordet, HUB-University Hospital of Brussels, 90 Rue Meylemeersch, 1070, Brussels, Belgium.
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6
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Gaur S, Panda A, Fajardo JE, Hamilton J, Jiang Y, Gulani V. Magnetic Resonance Fingerprinting: A Review of Clinical Applications. Invest Radiol 2023; 58:561-577. [PMID: 37026802 PMCID: PMC10330487 DOI: 10.1097/rli.0000000000000975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
ABSTRACT Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.
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Affiliation(s)
- Sonia Gaur
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Ananya Panda
- All India Institute of Medical Sciences, Jodhpur, Rajasthan, India
| | | | - Jesse Hamilton
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Yun Jiang
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
| | - Vikas Gulani
- Department of Radiology, Michigan Medicine, Ann Arbor, MI
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7
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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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8
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Dinis Fernandes C, Schaap A, Kant J, van Houdt P, Wijkstra H, Bekers E, Linder S, Bergman AM, van der Heide U, Mischi M, Zwart W, Eduati F, Turco S. Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer. Cancers (Basel) 2023; 15:3074. [PMID: 37370685 DOI: 10.3390/cancers15123074] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 05/11/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.
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Affiliation(s)
- Catarina Dinis Fernandes
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Annekoos Schaap
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Joan Kant
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Petra van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
- Department of Urology, Amsterdam University Medical Centers, 1100 DD Amsterdam, The Netherlands
| | - Elise Bekers
- Department of Pathology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Simon Linder
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Andries M Bergman
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Division of Medical Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Uulke van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Massimo Mischi
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Wilbert Zwart
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Division of Oncogenomics, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Federica Eduati
- Biomedical Engineering-Computational Biology Department, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
- Institute for Complex Molecular Systems, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
| | - Simona Turco
- Electrical Engineering Department, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands
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9
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Bengtsson J, Thimansson E, Baubeta E, Zackrisson S, Sundgren PC, Bjartell A, Flondell-Sité D. Correlation between ADC, ADC ratio, and Gleason Grade group in prostate cancer patients undergoing radical prostatectomy: Retrospective multicenter study with different MRI scanners. Front Oncol 2023; 13:1079040. [PMID: 36890837 PMCID: PMC9986526 DOI: 10.3389/fonc.2023.1079040] [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: 10/24/2022] [Accepted: 02/03/2023] [Indexed: 02/22/2023] Open
Abstract
Background MRI is an important tool in the prostate cancer work-up, with special emphasis on the ADC sequence. This study aimed to investigate the correlation between ADC and ADC ratio compared to tumor aggressiveness determined by a histopathological examination after radical prostatectomy. Methods Ninety-eight patients with prostate cancer underwent MRI at five different hospitals prior to radical prostatectomy. Images were retrospectively analyzed individually by two radiologists. The ADC of the index lesion and reference tissues (contralateral normal prostatic, normal peripheral zone, and urine) was recorded. Absolute ADC and different ADC ratios were compared to tumor aggressivity according to the ISUP Gleason Grade Groups extracted from the pathology report using Spearman's rank correlation coefficient (ρ). ROC curves were used to evaluate the ability to discriminate between ISUP 1-2 and ISUP 3-5 and intra class correlation and Bland-Altman plots for interrater reliability. Results All patients had prostate cancer classified as ISUP grade ≥ 2. No correlation was found between ADC and ISUP grade. We found no benefit of using the ADC ratio over absolute ADC. The AUC for all metrics was close to 0.5, and no threshold could be extracted for prediction of tumor aggressivity. The interrater reliability was substantial to almost perfect for all variables analyzed. Conclusions ADC and ADC ratio did not correlate with tumor aggressiveness defined by ISUP grade in this multicenter MRI study. The result of this study is opposite to previous research in the field.
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Affiliation(s)
- Johan Bengtsson
- Department of Clinical Sciences, Radiology, Lund, Lund University, Lund, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Erik Thimansson
- Department of Translational Medicine, Lund University, Malmö, Sweden.,Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Erik Baubeta
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.,Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Sophia Zackrisson
- Department of Translational Medicine, Lund University, Malmö, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Malmö, Sweden
| | - Pia Charlotte Sundgren
- Department of Clinical Sciences, Radiology, Lund, Lund University, Lund, Sweden.,Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.,Lund Bioimaging Center (LBIC), Lund University, Lund, Sweden
| | - Anders Bjartell
- Department of Translational Medicine, Lund University, Malmö, Sweden.,Department of Urology, Skåne University Hospital, Malmö, Sweden
| | - Despina Flondell-Sité
- Department of Translational Medicine, Lund University, Malmö, Sweden.,Department of Urology, Skåne University Hospital, Malmö, Sweden
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10
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Cai L, Li X, Wu L, Wang B, Si M, Tao X. A Prognostic Model Generated from an Apparent Diffusion Coefficient Ratio Reliably Predicts the Outcomes of Oral Tongue Squamous Cell Carcinoma. Curr Oncol 2022; 29:9031-9045. [PMID: 36547122 PMCID: PMC9777250 DOI: 10.3390/curroncol29120708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/07/2022] [Accepted: 11/16/2022] [Indexed: 11/23/2022] Open
Abstract
This study aimed to develop an apparent diffusion coefficient (ADC) ratio-based prognostic model to predict the recurrence and disease-free survival (DFS) of oral tongue squamous cell carcinoma (OTSCC). A total of 188 patients with cT1-2 oral tongue squamous cell carcinoma were enrolled retrospectively. Clinical and laboratory data were extracted from medical records. The ADC values were measured at the regions of interest of the tumor and non-tumor tissues of the MRI images, and the ADC ratio was used for comparison between the patient with recurrence (n = 83 case, 44%) and patients without recurrence (n = 105 cases, 56%). Cox proportional hazards models were generated to analyze the risk factors of cancer recurrence. A nomogram was developed based on significant risk factors to predict 1-, 5- and 10-year DFS. The receiver operator characteristic (ROC) curves of predictors in the multivariable Cox proportional hazards prognostic model were generated to predict the recurrence and DFS. The integrated areas under the ROC curve were calculated to evaluate discrimination of the models. The ADC ratio, tumor thickness and lymph node ratio were reliable predictors in the final prognostic model. The final model had a 71.1% sensitivity and an 81.0% specificity. ADC ratio was the strongest predictor of cancer recurrence in prognostic performance. Discrimination and calibration statistics were satisfactory with C-index above 0.7 for both model development and internal validation. The calibration curve showed that the 5- and 10-year DFS predicted by the nomogram agreed with actual observations.
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Affiliation(s)
- Lingling Cai
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Xiaoguang Li
- Department of Oral Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Disease, Shanghai 201999, China
- Department of Otolaryngology-Head and Neck Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Lizhong Wu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Bocheng Wang
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
| | - Mingjue Si
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
| | - Xiaofeng Tao
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201999, China
- Correspondence: (M.S.); (X.T.)
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11
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Lo WC, Panda A, Jiang Y, Ahad J, Gulani V, Seiberlich N. MR fingerprinting of the prostate. MAGMA (NEW YORK, N.Y.) 2022; 35:557-571. [PMID: 35419668 DOI: 10.1007/s10334-022-01012-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 06/03/2023]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has been adopted as the key tool for detection, localization, characterization, and risk stratification of patients suspected to have prostate cancer. Despite advantages over systematic biopsy, the interpretation of prostate mpMRI has limitations including a steep learning curve, leading to considerable interobserver variation. There is growing interest in clinical translation of quantitative imaging techniques for more objective lesion assessment. However, traditional mapping techniques are slow, precluding their use in the clinic. Magnetic resonance fingerprinting (MRF) is an efficient approach for quantitative maps of multiple tissue properties simultaneously. The T1 and T2 values obtained with MRF have been validated with phantom studies as well as in normal volunteers and patients. Studies have shown that MRF-derived T1 and T2 along with ADC values are all significant independent predictors in the differentiation between normal prostate tissue and prostate cancer, and hold promise in differentiating low and intermediate/high-grade cancers. This review seeks to introduce the basics of the prostate MRF technique, discuss the potential applications of prostate MRF for the characterization of prostate cancer, and describes ongoing areas of research.
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Affiliation(s)
- Wei-Ching Lo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Siemens Medical Solutions USA, Boston, Massachusetts, USA
| | - Ananya Panda
- Department of Radiology, Mayo Clinic, 200 1st Street SW, Rochester, MN, 55905, USA
| | - Yun Jiang
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - James Ahad
- Case Western Reserve University, Cleveland, OH, USA
| | - Vikas Gulani
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA
| | - Nicole Seiberlich
- Department of Radiology, University of Michigan, University of Michigan Health System, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5030, USA.
- Case Western Reserve University, Cleveland, OH, USA.
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12
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The current role of MRI for guiding active surveillance in prostate cancer. Nat Rev Urol 2022; 19:357-365. [PMID: 35393568 DOI: 10.1038/s41585-022-00587-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2022] [Indexed: 01/13/2023]
Abstract
Active surveillance (AS) is the recommended treatment option for low-risk and favourable intermediate-risk prostate cancer management, preserving oncological and functional outcomes. However, active monitoring using relevant parameters in addition to the usual clinical, biological and pathological considerations is necessary to compensate for initial undergrading of the tumour or to detect early progression without missing the opportunity to provide curative therapy. Indeed, several studies have raised concerns about inadequate biopsy sampling at diagnosis. However, the implementation of baseline MRI and targeted biopsy have led to improved initial stratification of low-risk disease; baseline MRI correlates well with disease characteristics and AS outcomes. The use of follow-up MRI during the surveillance phase also raises the question of the requirement for serial biopsies in the absence of radiological progression and the possibility of using completely MRI-based surveillance, with triggers for biopsies based solely on MRI findings. This concept of a tailored-risk, imaging-based monitoring strategy is aimed at reducing invasive procedures. However, the abandonment of serial biopsies in the absence of MRI progression can probably not yet be recommended in routine practice, as the data from real-life cohorts are heterogeneous and inconclusive. Thus, the evolution towards a routine, fully MRI-guided AS pathway has to be preceded by ensuring quality programme assessment for MRI reading and by demonstrating its safety in prospective trials.
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13
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Gunashekar DD, Bielak L, Hägele L, Oerther B, Benndorf M, Grosu AL, Brox T, Zamboglou C, Bock M. Explainable AI for CNN-based prostate tumor segmentation in multi-parametric MRI correlated to whole mount histopathology. Radiat Oncol 2022; 17:65. [PMID: 35366918 PMCID: PMC8976981 DOI: 10.1186/s13014-022-02035-0] [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: 01/03/2022] [Accepted: 03/15/2022] [Indexed: 12/15/2022] Open
Abstract
Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data is used as input, and the segmentation output is difficult to verify due to the lack of clinically established ground truth images. In this work we use an explainable deep learning model to interpret the predictions of a convolutional neural network (CNN) for prostate tumor segmentation. The CNN uses a U-Net architecture which was trained on multi-parametric MRI data from 122 patients to automatically segment the prostate gland and prostate tumor lesions. In addition, co-registered ground truth data from whole mount histopathology images were available in 15 patients that were used as a test set during CNN testing. To be able to interpret the segmentation results of the CNN, heat maps were generated using the Gradient Weighted Class Activation Map (Grad-CAM) method. The CNN achieved a mean Dice Sorensen Coefficient 0.62 and 0.31 for the prostate gland and the tumor lesions -with the radiologist drawn ground truth and 0.32 with whole-mount histology ground truth for tumor lesions. Dice Sorensen Coefficient between CNN predictions and manual segmentations from MRI and histology data were not significantly different. In the prostate the Grad-CAM heat maps could differentiate between tumor and healthy prostate tissue, which indicates that the image information in the tumor was essential for the CNN segmentation.
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14
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Sushentsev N, Caglic I, Rundo L, Kozlov V, Sala E, Gnanapragasam VJ, Barrett T. Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression. Br J Radiol 2022; 95:20210842. [PMID: 34538077 PMCID: PMC8978242 DOI: 10.1259/bjr.20210842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/03/2021] [Accepted: 08/19/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE To analyse serial changes in MRI-derived tumour measurements and apparent diffusion coefficient (ADC) values in prostate cancer (PCa) patients on active surveillance (AS) with and without histopathological disease progression. METHODS This study included AS patients with biopsy-proven PCa with a minimum of two consecutive MR examinations and at least one repeat targeted biopsy. Tumour volumes, largest axial two-dimensional (2D) surface areas, and maximum diameters were measured on T2 weighted images (T2WI). ADC values were derived from the whole lesions, 2D areas, and small-volume regions of interest (ROIs) where tumours were most conspicuous. Areas under the ROC curve (AUCs) were calculated for combinations of T2WI and ADC parameters with optimal specificity and sensitivity. RESULTS 60 patients (30 progressors and 30 non-progressors) were included. In progressors, T2WI-derived tumour volume, 2D surface area, and maximum tumour diameter had a median increase of +99.5%,+55.3%, and +21.7% compared to +29.2%,+8.1%, and +6.9% in non-progressors (p < 0.005 for all). Follow-up whole-volume and small-volume ROIs ADC values were significantly reduced in progressors (-11.7% and -9.5%) compared to non-progressors (-6.1% and -1.6%) (p < 0.05 for both). The combined AUC of a relative increase in maximum tumour diameter by 20% and reduction in small-volume ADC by 10% was 0.67. CONCLUSION AS patients show significant differences in tumour measurements and ADC values between those with and without histopathological disease progression. ADVANCES IN KNOWLEDGE This paper proposes specific clinical cut-offs for T2WI-derived maximum tumour diameter (+20%) and small-volume ADC (-10%) to predict histopathological PCa progression on AS and supplement subjective serial MRI assessment.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
| | - Iztok Caglic
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
| | | | - Vasily Kozlov
- Department of Public Health and Healthcare Organisation, Sechenov First Moscow State Medical University, Moscow, Russia
| | | | | | - Tristan Barrett
- Department of Radiology, Addenbrooke’s Hospital and University of Cambridge, Cambridge, UK
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15
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Falaschi Z, Tricca S, Attanasio S, Billia M, Airoldi C, Percivale I, Bor S, Perri D, Volpe A, Carriero A. Non-timely clinically applicable ADC ratio in prostate mpMRI: a comparison with fusion biopsy results. Abdom Radiol (NY) 2022; 47:3855-3867. [PMID: 35943517 PMCID: PMC9560938 DOI: 10.1007/s00261-022-03627-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE The purpose of the study was to assess the diagnostic accuracy of ADC ratio and to evaluate its efficacy in reducing the number of false positives in prostatic mpMRI. MATERIALS AND METHODS All patients who underwent an mpMRI and a targeted fusion biopsy in our institution from 2016 to 2021 were retrospectively selected. Two experienced readers (R1 and R2) independently evaluated the images, blindly to biopsy results. The radiologists assessed the ADC ratios by tracing a circular 10 mm2 ROI on the biopsied lesion and on the apparently benign contralateral parenchyma. Prostate cancers were divided into non-clinically significant (nsPC, Gleason score = 6) and clinically significant (sPC, Gleason score ≥ 7). ROC analyses were performed. RESULTS 167 patients and188 lesions were included. Concordance was 0.62 according to Cohen's K. ADC ratio showed an AUC for PCAs of 0.78 in R1 and 0.8 in R2. The AUC for sPC was 0.85 in R1 and 0.84 in R2. The 100% sensitivity cut-off for sPCs was 0.65 (specificity 25.6%) in R1 and 0.66 (specificity 27.4%) in R2. Forty-three benign or not clinically significant lesions were above the 0.65 threshold in R1; 46 were above the 0.66 cut-off in R2. This would have allowed to avoid an equal number of unnecessary biopsies at the cost of 2 nsPCs in R1 and one nsPC in R2. CONCLUSION In our sample, the ADC ratio was a useful and accurate tool that could potentially reduce the number of false positives in mpMRI.
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Affiliation(s)
- Zeno Falaschi
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Stefano Tricca
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy.
| | - Silvia Attanasio
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Michele Billia
- Department of Urology, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Chiara Airoldi
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Ilaria Percivale
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Simone Bor
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Davide Perri
- Department of Urology, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Alessandro Volpe
- Department of Urology, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
| | - Alessandro Carriero
- Department of Diagnosis and Treatment Services, Radiodiagnostics, Azienda Ospedaliero Universitaria Maggiore della Carità, Corso Mazzini 18, 28100, Novara, Italy
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16
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Boschheidgen M, Schimmöller L, Arsov C, Ziayee F, Morawitz J, Valentin B, Radke KL, Giessing M, Esposito I, Albers P, Antoch G, Ullrich T. MRI grading for the prediction of prostate cancer aggressiveness. Eur Radiol 2021; 32:2351-2359. [PMID: 34748064 PMCID: PMC8921105 DOI: 10.1007/s00330-021-08332-8] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/15/2021] [Accepted: 09/06/2021] [Indexed: 01/01/2023]
Abstract
OBJECTIVES T o evaluate the value of multiparametric MRI (mpMRI) for the prediction of prostate cancer (PCA) aggressiveness. METHODS In this single center cohort study, consecutive patients with histologically confirmed PCA were retrospectively enrolled. Four different ISUP grade groups (1, 2, 3, 4-5) were defined and fifty patients per group were included. Several clinical (age, PSA, PSAD, percentage of PCA infiltration) and mpMRI parameters (ADC value, signal increase on high b-value images, diameter, extraprostatic extension [EPE], cross-zonal growth) were evaluated and correlated within the four groups. Based on combined descriptors, MRI grading groups (mG1-mG3) were defined to predict PCA aggressiveness. RESULTS In total, 200 patients (mean age 68 years, median PSA value 8.1 ng/ml) were analyzed. Between the four groups, statistically significant differences could be shown for age, PSA, PSAD, and for MRI parameters cross-zonal growth, high b-value signal increase, EPE, and ADC (p < 0.01). All examined parameters revealed a significant correlation with the histopathologic biopsy ISUP grade groups (p < 0.01), except PCA diameter (p = 0.09). A mixed linear model demonstrated the strongest prediction of the respective ISUP grade group for the MRI grading system (p < 0.01) compared to single parameters. CONCLUSIONS MpMRI yields relevant pre-biopsy information about PCA aggressiveness. A combination of quantitative and qualitative parameters (MRI grading groups) provided the best prediction of the biopsy ISUP grade group and may improve clinical pathway and treatment planning, adding useful information beyond PI-RADS assessment category. Due to the high prevalence of higher grade PCA in patients within mG3, an early re-biopsy seems indicated in cases of negative or post-biopsy low-grade PCA. KEY POINTS • MpMRI yields relevant pre-biopsy information about prostate cancer aggressiveness. • MRI grading in addition to PI-RADS classification seems to be helpful for a size independent early prediction of clinically significant PCA. • MRI grading groups may help urologists in clinical pathway and treatment planning, especially when to consider an early re-biopsy.
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Affiliation(s)
- M Boschheidgen
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - L Schimmöller
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany.
| | - C Arsov
- Department of Urology, Medical Faculty, University Dusseldorf, 40225, Düsseldorf, Germany
| | - F Ziayee
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - J Morawitz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - B Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - K L Radke
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - M Giessing
- Department of Urology, Medical Faculty, University Dusseldorf, 40225, Düsseldorf, Germany
| | - I Esposito
- Department of Pathology, Medical Faculty, University Dusseldorf, 40225, Düsseldorf, Germany
| | - P Albers
- Department of Urology, Medical Faculty, University Dusseldorf, 40225, Düsseldorf, Germany
| | - G Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
| | - T Ullrich
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
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17
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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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18
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Hernando D, Zhang Y, Pirasteh A. Quantitative diffusion MRI of the abdomen and pelvis. Med Phys 2021; 49:2774-2793. [PMID: 34554579 DOI: 10.1002/mp.15246] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 08/05/2021] [Accepted: 09/15/2021] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.
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Affiliation(s)
- Diego Hernando
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Yuxin Zhang
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ali Pirasteh
- Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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19
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Maier SE, Wallström J, Langkilde F, Johansson J, Kuczera S, Hugosson J, Hellström M. Prostate Cancer Diffusion-Weighted Magnetic Resonance Imaging: Does the Choice of Diffusion-Weighting Level Matter? J Magn Reson Imaging 2021; 55:842-853. [PMID: 34535940 DOI: 10.1002/jmri.27895] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Diffusion-weighted magnetic resonance imaging plays an important role in multiparametric assessment of prostate lesions. The derived apparent diffusion coefficient (ADC) could be a useful quantitative biomarker for malignant growth, but lacks acceptance because of low reproducibility. PURPOSE To investigate the impact of the choice of diffusion-weighting levels (b-values) on contrast-to-noise ratio and quantitative measures in prostate diffusion-weighted MRI. STUDY TYPE Retrospective and simulation based on published data. SUBJECTS Patient cohort (21 men with Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score ≥3) from a single-center study. FIELD STRENGTH/SEQUENCE 3 T/diffusion-weighted imaging with single-shot echo-planar imaging. ASSESSMENT Both clinical data and simulations based on previously acquired data were used to quantify the influence of b-value choice in normal peripheral zone (PZ) and PZ tumor lesions. For clinical data, ADC was determined for different combinations of b-values. Contrast-to-noise ratio and quantitative diffusion measures were simulated for a wide range of b-values. STATISTICAL TESTS Tissue ADC and the lesion-to-normal tissue ADC ratios of different b-value combinations were compared with paired two-tailed Student's t-tests. A P-value <0.05 was considered statistically significant. RESULTS Findings about b-value dependence derived from clinical data and from simulations agreed with each other. Provided measurement was limited to two b-values, simulation-derived optimal b-value choices coincided with PI-RADSv2 recommendations. For two-point measurements, ADC decreased by 15% when the maximum b-value increased from 1000 to 1500 seconds/mm2 , but corresponding lesion-to-normal tissue ADC ratio showed no significant change (P = 0.86 for acquired data). Simulations with three or more measurement points produced ADCs that declined by only 8% over this range of maximum b-value. Corresponding ADC ratios declined between 2.6% (three points) and 3.8% (21 points). Simulations also revealed an ADC reduction of about 19% with the shorter echo and diffusion time evaluated. DATA CONCLUSION The comprehensive assessment of b-value dependence permits better formulation of protocol and analysis recommendations for obtaining reproducible results in prostate cancer diffusion-weighted MRI. LEVEL OF EVIDENCE 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Stephan E Maier
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonas Wallström
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Fredrik Langkilde
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Jens Johansson
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Stefan Kuczera
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jonas Hugosson
- Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Urology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - Mikael Hellström
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Department of Radiology, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
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20
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The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging. Pol J Radiol 2021; 86:e262-e268. [PMID: 34136043 PMCID: PMC8186305 DOI: 10.5114/pjr.2021.106071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 09/05/2020] [Indexed: 12/02/2022] Open
Abstract
Purpose This study has focused on investigating the relationship between the exponential apparent diffusion coefficient (exp-ADC), selective apparent diffusion coefficient (sel-ADC) values, the ADC ratio (ADCr), and prostate cancer aggressiveness with transrectal ultrasound-guided prostate biopsy in patients with prostate cancer. Material and methods All patients underwent a multiparametric magnetic resonance imaging (mpMRI) including tri-planar T2-weighted (T2W), dynamic contrast-enhanced (DCE), diffusion-weighted sequences using a 3.0-Tesla MR scanner (Skyra, Siemens Medical Systems, Germany) with a dedicated 18-channel body coil and a spine coil underneath the pelvis, with the patient in the supine position. Exp-ADC, sel-ADC, and ADCr of defined lesions were evaluated using region-of-interest-based measurements. Exp-ADC, sel-ADC, and ADCr were correlated with the Gleason score obtained through transrectal ultrasound-guided biopsy. Results Patients were divided into 2 groups. Group I is Gleason score ≥ 3 + 4, group II is Gleason score = 6. Sel-ADC and exp-ADC were statistically significant between 2 groups (0.014 and 0.012, respectively). However, the ADCr difference between nonclinical significant prostate cancer from clinically significant prostate cancer was not significant (p = 0.09). Conclusions This study is the first to evaluate exp-ADC and sel-ADC values of prostate carcinoma with ADCr. One limitation of this study might be the limited number of patients. Exp-ADC and sel-ADC values in prostate MRI imaging improved the specificity, accuracy, and area under the curve (AUC) for detecting clinically relevant prostate carcinoma. Adding exp-ADC and sel-ADC values to ADCr can be used to increase the diagnostic accuracy of DWI.
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21
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Scialpi M, Scialpi P, Martorana E, Torre R, Improta A, Aisa MC, D’Andrea A, Di Blasi A. Simplified PI-RADS (S-PI-RADS) for biparametric MRI to detect and manage prostate cancer: What urologists need to know. Turk J Urol 2021; 47:175-182. [PMID: 35929870 PMCID: PMC8260088 DOI: 10.5152/tud.2021.21004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Accepted: 04/09/2021] [Indexed: 09/14/2023]
Abstract
Biparametric magnetic resonance imaging (bpMRI) of the prostate has emerged as an alternative to multiparametric MRI (mpMRI) for the detection of clinically significant prostate cancer (csPCa). However, while the Prostate Imaging Reporting and Data System (PI-RADS) is widely known for mpMRI, a proper PI-RADS for bpMRI has not yet been adopted. In this review, we report the current status and the future directions of bpMRI, and propose a simplified PI-RADS (S-PI-RADS) that could help radiologists and urologists in the detection and management of PCa.
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Affiliation(s)
- Michele Scialpi
- Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | - Pietro Scialpi
- Division of Urology, Portogruaro Hospital, Venice, Italy
| | | | - Riccardo Torre
- Division of Radiology, Ospedale Santa Maria, Terni, Italy
| | - Antonio Improta
- Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | - Maria Cristina Aisa
- Division of Obstetrics and Gynaecology, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, Perugia, Italy
| | | | - Aldo Di Blasi
- Division of Radiology, Tivoli Hospital, Tivoli, Italy
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22
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Yin H, Wang D, Yan R, Jin X, Hu Y, Zhai Z, Duan J, Zhang J, Wang K, Han D. Comparison of Diffusion Kurtosis Imaging and Amide Proton Transfer Imaging in the Diagnosis and Risk Assessment of Prostate Cancer. Front Oncol 2021; 11:640906. [PMID: 33937041 PMCID: PMC8082407 DOI: 10.3389/fonc.2021.640906] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/16/2021] [Indexed: 01/31/2023] Open
Abstract
Objectives This study aims to evaluate and compare the diagnostic value of DKI and APT in prostate cancer (PCa), and their correlation with Gleason Score (GS). Materials and Methods DKI and APT imaging of 49 patients with PCa and 51 patients with benign prostatic hyperplasia (BPH) were collected and analyzed, respectively. According to the GS, the patients with PCa were divided into high-risk, intermediate-risk and low-risk groups. The mean kurtosis (MK), mean diffusion (MD) and magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm) values among PCa, BPH, and different GS groups of PCa were compared and analyzed respectively. The diagnostic accuracy of each parameter was evaluated by using the receiver operating characteristic (ROC) curve. The correlation between each parameter and GS was analyzed by using Spearman’s rank correlation. Results The MK and MTRasym (3.5 ppm) values were significantly higher in PCa group than in BPH group, while the MD value was significantly lower than in BPH group. The differences of MK/MD/MTRasym (3.5 ppm) between any two of the low-risk, intermediate-risk, and high-risk groups were all statistically significant (p <0.05). The MK value showed the highest diagnostic accuracy in differentiating PCa and BPH, BPH and low-risk, low-risk and intermediate-risk, intermediate-risk and high-risk (AUC = 0.965, 0.882, 0.839, 0.836). The MK/MD/MTRasym (3.ppm) values showed good and moderate correlation with GS (r = 0.844, −0.811, 0.640, p <0.05), respectively. Conclusion DKI and APT imaging are valuable in the diagnosis of PCa and demonstrate strong correlation with GS, which has great significance in the risk assessment of PCa.
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Affiliation(s)
- Huijia Yin
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Dongdong Wang
- Department of Radiology, People's Hospital of Zhengzhou, Zhengzhou, China
| | - Ruifang Yan
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Xingxing Jin
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Ying Hu
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Zhansheng Zhai
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Jinhui Duan
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Jian Zhang
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Dongming Han
- Department of MR, The First Affiliated Hospital, Xinxiang Medical University, Weihui, China
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23
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Hearn N, Blazak J, Vivian P, Vignarajah D, Cahill K, Atwell D, Lagopoulos J, Min M. Prostate cancer GTV delineation with biparametric MRI and 68Ga-PSMA-PET: comparison of expert contours and semi-automated methods. Br J Radiol 2021; 94:20201174. [PMID: 33507812 DOI: 10.1259/bjr.20201174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The optimal method for delineation of dominant intraprostatic lesions (DIL) for targeted radiotherapy dose escalation is unclear. This study evaluated interobserver and intermodality variability of delineations on biparametric MRI (bpMRI), consisting of T2 weighted (T2W) and diffusion-weighted (DWI) sequences, and 68Ga-PSMA-PET/CT; and compared manually delineated GTV contours with semi-automated segmentations based on quantitative thresholding of intraprostatic apparent diffusion coefficient (ADC) and standardised uptake values (SUV). METHODS 16 patients who had bpMRI and PSMA-PET scanning performed prior to any treatment were eligible for inclusion. Four observers (two radiation oncologists, two radiologists) manually delineated the DIL on: (1) bpMRI (GTVMRI), (2) PSMA-PET (GTVPSMA) and (3) co-registered bpMRI/PSMA-PET (GTVFused) in separate sittings. Interobserver, intermodality and semi-automated comparisons were evaluated against consensus Simultaneous Truth and Performance Level Estimation (STAPLE) volumes, created from the relevant manual delineations of all observers with equal weighting. Comparisons included the Dice Similarity Coefficient (DSC), mean distance to agreement (MDA) and other metrics. RESULTS Interobserver agreement was significantly higher (p < 0.05) for GTVPSMA (DSC: 0.822, MDA: 1.12 mm) and GTVFused (DSC: 0.787, MDA: 1.34 mm) than for GTVMRI (DSC: 0.705, MDA 2.44 mm). Intermodality agreement between GTVMRI and GTVPSMA was low (DSC: 0.440, MDA: 4.64 mm). Agreement between semi-automated volumes and consensus GTV was low for MRI (DSC: 0.370, MDA: 8.16 mm) and significantly higher for PSMA-PET (0.571, MDA: 4.45 mm, p < 0.05). CONCLUSION 68Ga-PSMA-PET appears to improve interobserver consistency of DIL localisation vs bpMRI and may be more viable for simple quantitative delineation approaches; however, more sophisticated approaches to semi-automatic delineation factoring for patient- and disease-related heterogeneity are likely required. ADVANCES IN KNOWLEDGE This is the first study to evaluate the interobserver variability of prostate GTV delineations with co-registered bpMRI and 68Ga-PSMA-PET.
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Affiliation(s)
- Nathan Hearn
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
| | - John Blazak
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, Australia
| | - Philip Vivian
- Department of Medical Imaging, Sunshine Coast University Hospital, Birtinya, Australia
| | - Dinesh Vignarajah
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia
| | - Katelyn Cahill
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia
| | - Daisy Atwell
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
| | - Jim Lagopoulos
- University of the Sunshine Coast, Sippy Downs, Australia.,Sunshine Coast Mind and Neuroscience - Thompson Institute, University of the Sunshine Coast, Birtinya, Australia
| | - Myo Min
- Department of Radiation Oncology, Sunshine Coast University Hospital, Birtinya, Australia.,ICON Cancer Centre, Maroochydore, Australia.,University of the Sunshine Coast, Sippy Downs, Australia
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24
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Grönlund E, Almhagen E, Johansson S, Traneus E, Nyholm T, Thellenberg C, Ahnesjö A. Robust treatment planning of dose painting for prostate cancer based on ADC-to-Gleason score mappings - what is the potential to increase the tumor control probability? Acta Oncol 2021; 60:199-206. [PMID: 32941092 DOI: 10.1080/0284186x.2020.1817547] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
BACKGROUND AND PURPOSE The aim of this study was to evaluate the potential to increase the tumor control probability (TCP) with 'dose painting by numbers' (DPBN) plans optimized in a treatment planning system (TPS) compared to uniform dose plans. The DPBN optimization was based on our earlier published formalism for prostate cancer that is driven by dose-responses of Gleason scores mapped from apparent diffusion coefficients (ADC). MATERIAL AND METHODS For 17 included patients, a set of DPBN plans were optimized in a TPS by maximizing the TCP for an equal average dose to the prostate volume (CTVT) as for a conventional uniform dose treatment. For the plan optimizations we applied different photon energies, different precisions for the ADC-to-Gleason mappings, and different CTVT positioning uncertainties. The TCP increasing potential was evaluated by the DPBN efficiency, defined as the ratio of TCP increases for DPBN plans by TCP increases for ideal DPBN prescriptions (optimized without considering radiation transport phenomena, uncertainties of the CTVT positioning, and uncertainties of the ADC-to-Gleason mapping). RESULTS The median DPBN efficiency for the most conservative planning scenario optimized with a low precision ADC-to-Gleason mapping, and a positioning uncertainty of 0.6 cm was 10%, meaning that more than half of the patients had a TCP gain of at least 10% of the TCP for an ideal DPBN prescription. By increasing the precision of the ADC-to-Gleason mapping, and decreasing the positioning uncertainty the median DPBN efficiency increased by up to 40%. CONCLUSIONS TCP increases with DPBN plans optimized in a TPS were found more likely with a high precision mapping of image data into dose-responses and a high certainty of the tumor positioning. These findings motivate further development to ensure precise mappings of image data into dose-responses and to ensure a high spatial certainty of the tumor positioning when implementing DPBN clinically.
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Affiliation(s)
- Eric Grönlund
- Medical radiation sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Section of Medical Physics, Eskilstuna Hospital, Eskilstuna, Sweden
| | - Erik Almhagen
- Medical radiation sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- The Skandion Clinic, Uppsala, Sweden
| | - Silvia Johansson
- Uppsala University Hospital, Uppsala, Sweden
- Experimental and clinical oncology, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | | | - Anders Ahnesjö
- Medical radiation sciences, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
- Uppsala University Hospital, Uppsala, Sweden
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25
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Wang X, Hielscher T, Radtke JP, Görtz M, Schütz V, Kuder TA, Gnirs R, Schwab C, Stenzinger A, Hohenfellner M, Schlemmer HP, Bonekamp D. Comparison of single-scanner single-protocol quantitative ADC measurements to ADC ratios to detect clinically significant prostate cancer. Eur J Radiol 2021; 136:109538. [PMID: 33482592 DOI: 10.1016/j.ejrad.2021.109538] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/28/2020] [Accepted: 01/07/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Mean ADC has high predictive value for the presence of clinically significant prostate cancer (sPC). Measurement variability is introduced by different scanners, protocols, intra-and inter-patient variation. Internal calibration by ADC ratios can address such fluctuations however can potentially lower the biological value of quantitative ADC determination by being sensitive to deviations in reference tissue signal. PURPOSE To better understand the predictive value of quantitative ADC measurements in comparison to internal reference ratios when measured in a single scanner, single protocol setup. MATERIALS AND METHODS 284 consecutive patients who underwent 3 T MRI on a single scanner followed by MRI-transrectal ultrasound fusion biopsy were included. A board-certified radiologist retrospectively reviewed all MRIs blinded to clinical information and placed regions of interest (ROI) on all focal lesions and the following reference regions: normal-appearing peripheral zone (PZNL) and transition zone (TZNL), the urinary bladder (BLA), and right and left internal obturator muscle (RIOM, LIOM). ROI-based mean ADC and ADC ratios to the reference regions were compared regarding their ability to predict the aggressiveness of prostate cancer. Spearman's rank correlation coefficient was used to estimate the correlation between ADC parameters, Gleason score (GS) and ADC ratios. The primary endpoint was presence of sPC, defined as a GS ≥ 3 + 4. Univariable and multivariable logistic regression models were constructed to predict sPC. Receiver operating characteristics curves (ROC) were used for visualization; DeLong test was used to evaluate the differences of the area under the curve (AUC). Bias-corrected AUC values and corresponding 95 %-CI were calculated using bootstrapping with 100 bootstrap samples. RESULTS After exclusion of patients who received prior treatment, 259 patients were included in the final cohort of which 220 harbored 351 MR lesions. Mean ADC and ADC ratios demonstrated a negative correlation with the GS. Mean ADC had the strongest correlation with ρ of -0.34, followed by ADCratioPZNL (ρ=-0.32). All ADC parameters except ADCratioLIOM (p = 0.07) were associated with sPC p<0.05). Mean ADC and ADCratioPZNL had the highest ROC AUC of all parameters (0.68). Multivariable models with mean ADC improve predictive performance. CONCLUSIONS A highly standardized single-scanner mean ADC measurement could not be improved upon using any of the single ADC ratio parameters or combinations of these parameters in predicting the aggressiveness of prostate cancer.
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Affiliation(s)
- Xianfeng Wang
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Radiology, Affiliated Hospital of Guilin Medical University, Guangxi, Guilin, PR China
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jan Philipp Radtke
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Magdalena Görtz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Viktoria Schütz
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Tristan Anselm Kuder
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Regula Gnirs
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Constantin Schwab
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Markus Hohenfellner
- Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany
| | - David Bonekamp
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; German Cancer Consortium (DKTK), Germany.
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26
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Palumbo P, Manetta R, Izzo A, Bruno F, Arrigoni F, De Filippo M, Splendiani A, Di Cesare E, Masciocchi C, Barile A. Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement. Gland Surg 2020; 9:2235-2247. [PMID: 33447576 DOI: 10.21037/gs-20-547] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.
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Affiliation(s)
- Pierpaolo Palumbo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Rosa Manetta
- Radiology Unit, San Salvatore Hospital, L'Aquila, Italy
| | - Antonio Izzo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Federico Bruno
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Francesco Arrigoni
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Massimo De Filippo
- Department of Medicine and Surgery (DiMec), Section of Radiology, University of Parma, Maggiore Hospital, Parma, Italy
| | - Alessandra Splendiani
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Carlo Masciocchi
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Antonio Barile
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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27
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Klingebiel M, Ullrich T, Quentin M, Bonekamp D, Aissa J, Mally D, Arsov C, Albers P, Antoch G, Schimmöller L. Advanced diffusion weighted imaging of the prostate: Comparison of readout-segmented multi-shot, parallel-transmit and single-shot echo-planar imaging. Eur J Radiol 2020; 130:109161. [DOI: 10.1016/j.ejrad.2020.109161] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 06/30/2020] [Indexed: 01/21/2023]
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Barral M, Jemal-Turki A, Beuvon F, Soyer P, Camparo P, Cornud F. Cellular density of low-grade transition zone prostate cancer: A limiting factor to correlate restricted diffusion with tumor aggressiveness. Eur J Radiol 2020; 131:109230. [PMID: 32866908 DOI: 10.1016/j.ejrad.2020.109230] [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: 06/30/2020] [Revised: 08/11/2020] [Accepted: 08/16/2020] [Indexed: 12/24/2022]
Abstract
OBJECTIVE To compare the mean apparent diffusion coefficient (ADCmean) and glandular density of Gleason score (GS) 3 + 3 transition zone prostate cancers (TZ-PCa) with those of the peripheral zone (PZ-PCa). MATERIAL & METHODS Seventy-nine men (mean age: 65 ± 6 [SD] years; range: 52-81 years) with 37 TZ-PCa (37/79; 53 %) and 42 PZ-PCa (42/79; 47 %) had prostate MRI before radical prostatectomy. Glandular cell density was semi-quantitatively evaluated in all tumors. ADCmean and glandular cell density of GS3 + 3 TZ-PCa were compared to those of PZ-PCa. ADCmean was correlated to GS in each zone. RESULTS ADCmean of GS 3 + 3 tumors was significantly lower in the TZ (728 × 10-6±52 [SD] mm²/s; range: 670-1060mm²/s) than in the PZ (865 × 10-6 ±121 [SD] mm²/s; range: 670-1120mm²/s) (p = 0.0007), related to a significantly higher glandular density involving more than 50 % of the tumor in 58 % (7/12) of patients in GS3 + 3 TZ-PCa versus 7.6 % (1/13) in PZ-PCa (p = 0.03). ADCmean of GS3 + 3 TZ-PCa was not significantly different from that of GS 3 + 4 (p = 0.14) or GS>3 + 4 Ca (p = 0.9), whatever the zone of origin. In the PZ, ADCmean of GS 3 + 3-PCa was higher than that of Gleason>3 + 4 PZ-PCa (p = 0.02) and similar to that of GS 3 + 4 PZ-PCa (p = 0.24). Correlation between ADCmean and GS was weak for TZ-PCa (ρ = 0.32; p = 0.04) and moderate for PZ-PCa (ρ = 0.45; p = 0.003). CONCLUSION ADCmean of GS 3 + 3 TZ-PCa is significantly lower than that of GS 3 + 3 PZ-PCa, related to a unique dense histological pattern and reaches that of higher-grade PCa, whatever the zone of origin.
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Affiliation(s)
- Matthias Barral
- Hôpital Cochin-APHP, Department of Radiology, 27 rue du Fbg St Jacques, 75014, Paris, France.
| | - Aida Jemal-Turki
- Hôpital Cochin-APHP, Department of Radiology, 27 rue du Fbg St Jacques, 75014, Paris, France.
| | - Frédéric Beuvon
- Hôpital Cochin-APHP, Department of Pathology, 27 rue du Fbg St Jacques, 75014, Paris, France.
| | - Philippe Soyer
- Hôpital Cochin-APHP, Department of Radiology, 27 rue du Fbg St Jacques, 75014, Paris, France.
| | - Philippe Camparo
- Centre de Pathologie, 51 rue Jeanne d'Arc, 80000, Amiens, France.
| | - François Cornud
- Clinique de l'Alma, 166 rue de l'Université, 75007, Paris, France.
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29
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Falaschi Z, Valenti M, Lanzo G, Attanasio S, Valentini E, García Navarro LI, Aquilini F, Stecco A, Carriero A. Accuracy of ADC ratio in discriminating true and false positives in multiparametric prostatic MRI. Eur J Radiol 2020; 128:109024. [PMID: 32387923 DOI: 10.1016/j.ejrad.2020.109024] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 01/17/2023]
Abstract
PURPOSE Our goal was to evaluate the usefulness of apparent diffusion coefficient (ADC) ratios in discriminating true from false positives in multiparametric (mp) prostate MRI in clinical practice. METHODS We retrospectively evaluated 98 prostate lesions in a series of 73 patients who had undergone prostate mpMRI and standard 12-core prostatic biopsy in our institution from 2016 to 2018. Two experienced radiologists performed double blind ADC value quantifications of both MRI-identified lesions and apparently benign contralateral prostatic parenchyma in a circular region of interest (ROI) of ∼10 mm2. The ratios between the mean values of both measurements (i.e., ADC ratio mean) and between the minimum value of the lesion and the maximum value of the benign parenchyma (i.e., ADC ratio min-max) were automatically calculated. The malignancy of all lesions was determined through biopsy according to Gleason score (GS ≥ 6) and localization. RESULTS For Reader 1, the area under the ROC curve (AUC) of ADC ratio mean and ADC ratio min-max were 0.72 and 0.67, respectively, whereas for Reader 2 these values were 0.74 and 0.71, respectively. The best cut-off values for ADC ratio means were ≥ 0.5 (Reader 1) and ≥ 0.6 (Reader 2), with a sensitivity of 76.3 % and 84.2 % and a specificity of 51.7 % and 50 %, respectively. Moreover, based on a threshold of 0.6, no clinically significant prostate cancer (csPCa) was missed by Reader 1, while only one went unnoticed by Reader 2. CONCLUSION The ADC ratio is a useful and moderately accurate complementary tool to diagnose prostate cancer in the mp-MRI.
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Affiliation(s)
- Zeno Falaschi
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy.
| | - Martina Valenti
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy
| | - Giuseppe Lanzo
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy
| | - Silvia Attanasio
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy
| | - Eleonora Valentini
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy
| | | | | | - Alessandro Stecco
- Azienda Ospedaliero-Universitaria Maggiore della Carita, Novara, NO, Italy
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Israël B, Leest MVD, Sedelaar M, Padhani AR, Zámecnik P, Barentsz JO. Multiparametric Magnetic Resonance Imaging for the Detection of Clinically Significant Prostate Cancer: What Urologists Need to Know. Part 2: Interpretation. Eur Urol 2020; 77:469-480. [DOI: 10.1016/j.eururo.2019.10.024] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 10/21/2019] [Indexed: 01/08/2023]
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Cho J, Ahn H, Hwang SI, Lee HJ, Choe G, Byun SS, Hong SK. Biparametric versus multiparametric magnetic resonance imaging of the prostate: detection of clinically significant cancer in a perfect match group. Prostate Int 2020; 8:146-151. [PMID: 33425791 PMCID: PMC7767942 DOI: 10.1016/j.prnil.2019.12.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 12/12/2019] [Accepted: 12/28/2019] [Indexed: 11/16/2022] Open
Abstract
Background Biparametric (bp) magnetic resonance imaging (MRI) could be an alternative MRI for the detection of the clinically significant prostate cancer (csPCa). Purpose To compare the accuracies of prostate cancer detection and localization between prebiopsy bpMRI and postbiopsy multiparametric MRI (mpMRI) taken on different days, using radical prostatectomy specimens as the reference standards. Material and methods Data of 41 total consecutive patients who underwent the following examinations and procedures between September 2015 and March 2017 were collected: (1) magnetic resonance- and/or ultrasonography-guided biopsy after bpMRI; (2) postbiopsy mpMRI; and (3) radical prostatectomy with csPCa. Two radiologists scored suspected lesions on bpMRI and mpMRI independently using Prostate Imaging Reporting and Data System version 2. The diagnostic accuracy of detecting csPCa and the Dice similarity coefficient were obtained. Apparent diffusion coefficient (ADC) ratios were also obtained for quantitative comparison between bpMRI and mpMRI. Results Diagnostic accuracies on bpMRI and mpMRI were 0.83 and 0.82 for reader 1; 0.80 and 0.82 for reader 2. There are no significantly different values of diagnostic sensitivities or specificities between the readers or between MRI protocols. Intra-observer Dice similarity coefficient was significantly lower in reader 2, compared to that in reader 1 between the two MRI protocols. The range of mean ADC ratio was 0.281-0.635. There was no statistically significant difference in the ADC ratio between bpMRI and mpMRI. Conclusions Diagnostic performance of bpMRI without dynamic contrast enhancement MRI is not significantly different from mpMRI with dynamic contrast enhancement MRI in the detection of csPCa.
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Affiliation(s)
- Jungheum Cho
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Hyungwoo Ahn
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Sung Il Hwang
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Hak Jong Lee
- Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Gheeyoung Choe
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Seok-Soo Byun
- Department of Urology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
| | - Sung Kyu Hong
- Department of Urology, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea
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Hausmann D, Zoellner FG, Kubik-Huch RA. Editorial for "Qualitative and Quantitative Reporting of a Unique Biparametric MRI: Towards Biparametric MRI-Based Nomograms for Prediction of Prostate Biopsy Outcome in Men With a Clinical Suspicion of Prostate Cancer (IMPROD and MULTI-IMPROD Trials)". J Magn Reson Imaging 2019; 51:1568-1569. [PMID: 31675130 DOI: 10.1002/jmri.26980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 11/09/2022] Open
Abstract
LEVEL OF EVIDENCE 5 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:1568-1569.
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Affiliation(s)
- Daniel Hausmann
- Department of Radiology, Kantonsspital Baden, Baden, Switzerland.,Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frank Gerrit Zoellner
- Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany
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Shan Y, Chen X, Liu K, Zeng M, Zhou J. Prostate cancer aggressive prediction: preponderant diagnostic performances of intravoxel incoherent motion (IVIM) imaging and diffusion kurtosis imaging (DKI) beyond ADC at 3.0 T scanner with gleason score at final pathology. Abdom Radiol (NY) 2019; 44:3441-3452. [PMID: 31144091 DOI: 10.1007/s00261-019-02075-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
PURPOSE To explore the preponderant diagnostic performances of IVIM and DKI in predicting the Gleason score (GS) of prostate cancer. METHODS Diffusion-weighted imaging data were postprocessed using monoexponential, lVIM and DK models to quantitate the apparent diffusion coefficient (ADC), molecular diffusion coefficient (D), perfusion-related diffusion coefficient (Dstar), perfusion fraction (F), apparent diffusion for Gaussian distribution (Dapp), and apparent kurtosis coefficient (Kapp). Spearman's rank correlation coefficient was used to explore the relationship between those parameters and the GS, Kruskal-Wallis test, and Mann-Whitney U test were performed to compare the above parameters between the different groups, and a receiver-operating characteristic (ROC) curve was used to analyze the differential diagnosis ability. The interpretation of the results is in view of histopathologic tumor tissue composition. RESULTS The area under the ROC curves (AUCs) of ADC, F, D, Dapp, and Kapp in differentiating GS ≤ 3 + 4 and GS > 3 + 4 PCa were 0.744 (95% CI 0.581-0.868), 0.726 (95% CI 0.563-0.855), 0.732 (95% CI 0.569-0.860), and 0.752 (95% CI 0.590-0.875), 0.766 (95% CI 0.606-0.885), respectively, and those in differentiating GS ≤ 7 and GS > 7 PCa were 0.755 (95% CI 0.594-0.877), 0.734 (95% CI 0.571-0.861), 0.724 (95% CI0.560-0.853), and 0.716 (95% CI 0.552-0.847), 0.828 (95% CI 0.676-0.929), respectively. All the P values were less than 0.05. There was no significant difference in the AUC for the detection of different GS groups by using those parameters. CONCLUSION Both the IVIM and DKI models are beneficial to predict GS of PCa and indirectly predict its aggressiveness, and they have a comparable diagnostic performance with each other as well as ADC.
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Barile A, Brunese L, Giovagnoni A. Gland diseases: new perspectives in diagnostic radiology. Gland Surg 2019; 8:S126-S129. [PMID: 31559178 DOI: 10.21037/gs.2019.03.05] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Antonio Barile
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy.
| | - Luca Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, Campobasso, Italy.
| | - Andrea Giovagnoni
- Department of Radiology, Ospedali Riuniti, Università Politecnica Delle Marche, Ancona, Italy.
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Apparent Diffusion Coefficient (ADC) Ratio Versus Conventional ADC for Detecting Clinically Significant Prostate Cancer With 3-T MRI. AJR Am J Roentgenol 2019; 213:W134-W142. [DOI: 10.2214/ajr.19.21365] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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36
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Manetta R, Palumbo P, Gianneramo C, Bruno F, Arrigoni F, Natella R, Maggialetti N, Agostini A, Giovagnoni A, Di Cesare E, Splendiani A, Masciocchi C, Barile A. Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature. Gland Surg 2019; 8:S216-S222. [PMID: 31559188 DOI: 10.21037/gs.2019.05.02] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prostate cancer (PCa) is one of the most common cancers in male population. Multiparametric prostate magnetic resonance imaging (mp-MRI) has assumed a primary role in the diagnosis of PCa, combining morphological and functional data. Among different sequences, functional diffusion weighted imaging (DWI) is a powerful clinical tool which provides information about tissue on a cellular level. However, there is a considerable overlap between either BPH (Benign Prostate Hypertrophy) and prostatic cancer condition, as a different DWI signal intensity could be shown in the normal architecture gland. Apparent diffusion coefficient (ADC) has shown an increasing accuracy in addition to the DWI analysis in detection and localization of PCa. Notably, ADC maps derived DWI sequences has shown an overall high correlation with Gleason score (GS), considering the importance of an accurate grading of focal lesion, as main predictor factor. Furthermore, beyond the comparative analysis with DWI, ADC values has proven to be an useful marker of tumor aggressiveness, providing quantitative information on tumor characteristics according with GS and Gleason pattern, even more strenuous data are needed in order to verify which ADC analysis is more accurate.
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Affiliation(s)
- Rosa Manetta
- Division of Radiology, San Salvatore Hospital, L'Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Camilla Gianneramo
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Federico Bruno
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Francesco Arrigoni
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Raffaele Natella
- Radiology Department, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Nicola Maggialetti
- Department of Life and Health "V. Tiberio", University of Molise, Campobasso, Italy
| | - Andrea Agostini
- Department of Radiology, Ospedale Riuniti, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Giovagnoni
- Department of Radiology, Ospedale Riuniti, Università Politecnica delle Marche, Ancona, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Alessandra Splendiani
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Carlo Masciocchi
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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Abstract
BACKGROUND Multiparametric MRI (mpMRI) is currently the most accurate imaging modality for detection and local staging of prostate cancer (PCa). Disadvantages of this modality are high costs, time consumption and the need for a contrast medium. AIMS The aim of the work was to provide an overview of the current state of fast and contrast-free MRI imaging of the prostate. RESULTS Biparametric examination protocols and the use of three-dimensional T2-weighted sequences are readily available methods that can be used to shorten the examination time without sacrificing diagnostic accuracy.
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38
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Toivonen J, Montoya Perez I, Movahedi P, Merisaari H, Pesola M, Taimen P, Boström PJ, Pohjankukka J, Kiviniemi A, Pahikkala T, Aronen HJ, Jambor I. Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization. PLoS One 2019; 14:e0217702. [PMID: 31283771 PMCID: PMC6613688 DOI: 10.1371/journal.pone.0217702] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/16/2019] [Indexed: 12/19/2022] Open
Abstract
Purpose To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T2-weighted imaging (T2w), diffusion weighted imaging (DWI) acquired using high b values, and T2-mapping (T2). Methods T2w, DWI (12 b values, 0–2000 s/mm2), and T2 data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T2w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS. Results In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T2w, ADCm and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T2 mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments. Conclusion Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T2w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.
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Affiliation(s)
- Jussi Toivonen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- * E-mail:
| | - Ileana Montoya Perez
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Parisa Movahedi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Harri Merisaari
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Dept. of Future Technologies, University of Turku, Turku, Finland
- Turku PET Centre, University of Turku, Turku, Finland
| | - Marko Pesola
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, University of Turku and Dept. of Pathology, Turku University Hospital, Turku, Finland
| | | | | | - Aida Kiviniemi
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Tapio Pahikkala
- Dept. of Future Technologies, University of Turku, Turku, Finland
| | - Hannu J. Aronen
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Dept. of Diagnostic Radiology, University of Turku, Turku, Finland
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America
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Reda I, Khalil A, Elmogy M, Abou El-Fetouh A, Shalaby A, Abou El-Ghar M, Elmaghraby A, Ghazal M, El-Baz A. Deep Learning Role in Early Diagnosis of Prostate Cancer. Technol Cancer Res Treat 2019; 17:1533034618775530. [PMID: 29804518 PMCID: PMC5972199 DOI: 10.1177/1533034618775530] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.
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Affiliation(s)
- Islam Reda
- 1 Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.,2 Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | - Ashraf Khalil
- 3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Mohammed Elmogy
- 1 Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.,2 Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | | | - Ahmed Shalaby
- 2 Department of Bioengineering, University of Louisville, Louisville, KY, USA
| | | | - Adel Elmaghraby
- 5 Department of Computer Engineering and Computer Science, University of Louisville, Louisville, KY, USA
| | - Mohammed Ghazal
- 3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates
| | - Ayman El-Baz
- 2 Department of Bioengineering, University of Louisville, Louisville, KY, USA
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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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41
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Currin S, Flood TA, Krishna S, Ansari A, McInnes MD, Schieda N. Intraductal carcinoma of the prostate (IDC‐P) lowers apparent diffusion coefficient (ADC) values among intermediate risk prostate cancers. J Magn Reson Imaging 2018; 50:279-287. [DOI: 10.1002/jmri.26594] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 11/10/2018] [Accepted: 11/12/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Stephen Currin
- Department of Medical ImagingOttawa Hospital Ottawa Ontario Canada
| | - Trevor A. Flood
- Department of Anatomical PathologyOttawa Hospital Ottawa Ontario Canada
| | - Satheesh Krishna
- Department of Medical ImagingUniversity Health Network, University of Toronto Toronto Ontario Canada
| | - Afshin Ansari
- Department of Medical ImagingOttawa Hospital Ottawa Ontario Canada
| | | | - Nicola Schieda
- Department of Medical ImagingOttawa Hospital Ottawa Ontario Canada
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42
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Quantitative Apparent Diffusion Coefficient Derived From Diffusion-Weighted Imaging Has the Potential to Avoid Unnecessary MRI-Guided Biopsies of mpMRI-Detected PI-RADS 4 and 5 Lesions. Invest Radiol 2018; 53:736-741. [DOI: 10.1097/rli.0000000000000498] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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43
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Gennaro KH, Porter KK, Gordetsky JB, Galgano SJ, Rais-Bahrami S. Imaging as a Personalized Biomarker for Prostate Cancer Risk Stratification. Diagnostics (Basel) 2018; 8:diagnostics8040080. [PMID: 30513602 PMCID: PMC6316045 DOI: 10.3390/diagnostics8040080] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 11/13/2018] [Accepted: 11/15/2018] [Indexed: 02/07/2023] Open
Abstract
Biomarkers provide objective data to guide clinicians in disease management. Prostate-specific antigen serves as a biomarker for screening of prostate cancer but has come under scrutiny for detection of clinically indolent disease. Multiple imaging techniques demonstrate promising results for diagnosing, staging, and determining definitive management of prostate cancer. One such modality, multiparametric magnetic resonance imaging (mpMRI), detects more clinically significant disease while missing lower volume and clinically insignificant disease. It also provides valuable information regarding tumor characteristics such as location and extraprostatic extension to guide surgical planning. Information from mpMRI may also help patients avoid unnecessary biopsies in the future. It can also be incorporated into targeted biopsies as well as following patients on active surveillance. Other novel techniques have also been developed to detect metastatic disease with advantages over traditional computer tomography and magnetic resonance imaging, which primarily rely on defined size criteria. These new techniques take advantage of underlying biological changes in prostate cancer tissue to identify metastatic disease. The purpose of this review is to present literature on imaging as a personalized biomarker for prostate cancer risk stratification.
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Affiliation(s)
- Kyle H Gennaro
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Kristin K Porter
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Jennifer B Gordetsky
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
- Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Samuel J Galgano
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Soroush Rais-Bahrami
- Department of Urology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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Guo L, Zhang L, Zhao J. CT scan and magnetic resonance diffusion-weighted imaging in the diagnosis and treatment of esophageal cancer. Oncol Lett 2018; 16:7117-7122. [PMID: 30546446 PMCID: PMC6256330 DOI: 10.3892/ol.2018.9532] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 09/06/2018] [Indexed: 12/11/2022] Open
Abstract
Value of computed tomography (CT) scan and diffusion-weighted imaging (DWI) in the diagnosis and treatment of esophageal cancer was investigated. Seventy-eight patients with esophageal cancer treated in Jinan Central Hospital (Jinan, China) from January 2013 to June 2014 were selected. All patients underwent CT scan and DWI examination, and their clinical history data were analyzed. DWI was conducted. The short-term curative effect and the 3-year survival rate of patients in the high apparent diffusion coefficient (ADC) value group and the low ADC value group were compared; ADC values in the complete remission (CR) group and the partial remission (PR) group were compared. The difference in value between the length of esophageal lesions and the length of pathological specimens measured by CT scan was significantly different from that detected via DWI examination with b=600, 800 and 1,000 sec/mm2, respectively (P<0.05). The diagnostic rate of esophageal cancer via CT scan was significantly lower than that via DWI examination (P<0.05). After radiotherapy, the clinical control rate in the high ADC value group was significantly higher than that in the low ADC value group, and the 3-year survival rate in the former was significantly higher than that in the latter (P<0.05). In the 2nd week during radiotherapy and at the end of radiotherapy, the ADC values in the CR group were significantly higher than those in the PR group (P<0.05). In the 2nd week during radiotherapy and at the end of radiotherapy, ADC values were used to predict the CR rate of radiotherapy for esophageal cancer, and the areas under the receiver operating characteristic (ROC) curve were 0.776 and 0.935, respectively. Compared with CT scan, DWI has higher diagnostic rate and higher sensitivity. The length of esophageal tumor measured by DWI is close to that of pathological entity, which can guide the delineation of the target area of esophageal cancer.
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Affiliation(s)
- Lei Guo
- Department of MRI, Jinan Central Hospital, Jinan, Shandong 250013, P.R. China
| | - Liulong Zhang
- Department of Radiology, Dongying People's Hospital, Dongying, Shandong 257091, P.R. China
| | - Jianshe Zhao
- Department of Radiology, Jinan Children's Hospital, Jinan, Shandong 250022, P.R. China
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45
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Zhang Z, Xu H, Xue Y, Li J, Ye Q. Risk Stratification of Prostate Cancer Using the Combination of Histogram Analysis of Apparent Diffusion Coefficient Across Tumor Diffusion Volume and Clinical Information: A Pilot Study. J Magn Reson Imaging 2018; 49:556-564. [PMID: 30173421 DOI: 10.1002/jmri.26235] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 06/06/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND The effectiveness of quantitative MRI and clinical information in the risk stratification of prostate cancer (PCa) patients was evaluated separately in previous research; however, the differentiation power of combining quantitative MRI and clinical information has yet to be investigated. PURPOSE To investigate the power of combining histogram analysis of apparent diffusion coefficient (ADC) of tumor diffusion volume (tDv) with clinical information for the differentiation of low-grade (Gleason score [GS] ≤6) and high-grade (GS ≥7) PCa. STUDY TYPE Retrospective. POPULATION Fifty-nine PCa patients who underwent preoperative diffusion-weighted imaging (DWI) (acquired with b = 0, 1000 mm2 /s) and followed by radical prostatectomy within 6 months. SEQUENCES T2 -weighted, DWI, and ADC images at 3.0T. ASSESSMENT tDv defined with different ADC thresholds were analyzed for each patient and combined with age and prostate-specific antigen (PSA) level. Binary logistic regression with backward feature selection was applied to determine the best discrimination and corresponding combination of parameters. STATISTICAL TESTS Kolmogorov-Smirnov test; independent samples t-test; Mann-Whitney U-test; Spearman's rank correlation; receiver operating characteristic (ROC) analysis; binary logistical regression. RESULTS PSA and the 10th percentile ADC value of tDv defined with different diffusion thresholds were significantly different between low-grade and high-grade PCa groups (P < 0.05 for all). Median ADC of tDv based on a threshold of 1.008 × 10-3 mm2 /s exhibited the best performance (AUC = 0.86, 95% confidence interval [CI]: 0.75-0.94), whereas binary logistic regression with backward feature selection achieved 97.20% accuracy with AUC = 0.978 (95% CI: 0.929-0.997). DATA CONCLUSION The discriminatory power of a single histogram variable of ADC in tDv was not significantly superior to that of a single clinical parameter. The combination of histogram analysis of ADC of tDv and clinical information using logistic regression might significantly improve the risk stratification of PCa and achieve reasonably high accuracy. LEVEL OF EVIDENCE 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:556-564.
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Affiliation(s)
- Zhao Zhang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, ZheJiang Province, P.R. China
| | - Huazhi Xu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, ZheJiang Province, P.R. China
| | - Yingnan Xue
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, ZheJiang Province, P.R. China
| | - Jiance Li
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, ZheJiang Province, P.R. China
| | - Qiong Ye
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, ZheJiang Province, P.R. China
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Dinis Fernandes C, Dinh CV, Walraven I, Heijmink SW, Smolic M, van Griethuysen JJM, Simões R, Losnegård A, van der Poel HG, Pos FJ, van der Heide UA. Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2018; 7:9-15. [PMID: 33458399 PMCID: PMC7807756 DOI: 10.1016/j.phro.2018.06.005] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Revised: 06/06/2018] [Accepted: 06/14/2018] [Indexed: 11/30/2022]
Abstract
Background and purpose High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15–35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. Materials and methods In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient’s clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). Results A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. Conclusions These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.
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Affiliation(s)
| | - Cuong V Dinh
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Iris Walraven
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stijn W Heijmink
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Milena Smolic
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joost J M van Griethuysen
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.,GROW - School of Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Rita Simões
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Are Losnegård
- University of Bergen, Norway.,Haukeland University Hospital, Bergen, Norway
| | - Henk G van der Poel
- Department of Urology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Floris J Pos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
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Muehlematter UJ, Rupp NJ, Mueller J, Eberli D, Burger IA. 68Ga-PSMA PET/MR–Positive, Histopathology-Proven Prostate Cancer in a Patient With Negative Multiparametric Prostate MRI. Clin Nucl Med 2018; 43:e282-e284. [DOI: 10.1097/rlu.0000000000002143] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Is Prostate Imaging Reporting and Data System Version 2 Sufficiently Discovering Clinically Significant Prostate Cancer? Per-Lesion Radiology-Pathology Correlation Study. AJR Am J Roentgenol 2018; 211:114-120. [DOI: 10.2214/ajr.17.18684] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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49
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Nguyen TB, Ushinsky A, Yang A, Nguyentat M, Fardin S, Uchio E, Lall C, Lee T, Houshyar R. Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer. Br J Radiol 2018; 91:20180091. [PMID: 29869921 DOI: 10.1259/bjr.20180091] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient (ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions. METHODS A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest (ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC). RESULTS Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm2 s-1, 29.8%; and 1.135 × 10-3 mm2 s-1, 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm2 s-1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889. CONCLUSION Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.
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Affiliation(s)
- Tan B Nguyen
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Alexander Ushinsky
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Albert Yang
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Michael Nguyentat
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Sara Fardin
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Edward Uchio
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Chandana Lall
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Thomas Lee
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
| | - Roozbeh Houshyar
- 1 Radiological Sciences, University of California, Irvine Medical Center , Orange, CA , USA
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Hassanzadeh E, Alessandrino F, Olubiyi OI, Glazer DI, Mulkern RV, Fedorov A, Tempany CM, Fennessy FM. Comparison of quantitative apparent diffusion coefficient parameters with prostate imaging reporting and data system V2 assessment for detection of clinically significant peripheral zone prostate cancer. Abdom Radiol (NY) 2018; 43:1237-1244. [PMID: 28840280 DOI: 10.1007/s00261-017-1297-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
PURPOSE To compare diagnostic performance of PI-RADSv2 with ADC parameters to identify clinically significant prostate cancer (csPC) and to determine the impact of csPC definitions on diagnostic performance of ADC and PI-RADSv2. METHODS We retrospectively identified treatment-naïve pathology-proven peripheral zone PC patients who underwent 3T prostate MRI, using high b-value diffusion-weighted imaging from 2011 to 2015. Using 3D slicer, areas of suspected tumor (T) and normal tissue (N) on ADC (b = 0, 1400) were outlined volumetrically. Mean ADCT, mean ADCN, ADCratio (ADCT/ADCN) were calculated. PI-RADSv2 was assigned. Three csPC definitions were used: (A) Gleason score (GS) ≥ 4 + 3; (B) GS ≥ 3 + 4; (C) MRI-based tumor volume >0.5 cc. Performances of ADC parameters and PI-RADSv2 in identifying csPC were measured using nonparametric comparison of receiver operating characteristic curves using the area under the curve (AUC). RESULTS Eighty five cases met eligibility requirements. Diagnostic performances (AUC) in identifying csPC using three definitions were: (A) ADCT (0.83) was higher than PI-RADSv2 (0.65, p = 0.006); (B) ADCT (0.86) was higher than ADCratio (0.68, p < 0.001), and PI-RADSv2 (0.70, p = 0.04); (C) PI-RADSv2 (0.73) performed better than ADCratio (0.56, p = 0.02). ADCT performance was higher when csPC was defined by A or B versus C (p = 0.038 and p = 0.01, respectively). ADCratio performed better when csPC was defined by A versus C (p = 0.01). PI-RADSv2 performance was not affected by csPC definition. CONCLUSIONS When csPC was defined by GS, ADC parameters provided better csPC discrimination than PI-RADSv2, with ADCT providing best result. When csPC was defined by MRI-calculated volume, PI-RADSv2 provided better discrimination than ADCratio. csPC definition did not affect PI-RADSv2 diagnostic performance.
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Affiliation(s)
- Elmira Hassanzadeh
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
- Department of Surgery, University of Illinois at Chicago, 1200 W Harrison St, Chicago, IL, 60607, USA
| | - Francesco Alessandrino
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, USA.
| | - Olutayo I Olubiyi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
- Department of Radiology, Mercy Catholic Medical Center, 1500 Lansdowne Avenue, Darby, PA, USA
| | - Daniel I Glazer
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Robert V Mulkern
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, USA
| | - Andriy Fedorov
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Clare M Tempany
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Fiona M Fennessy
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
- Department of Imaging, Dana Farber Cancer Institute, Boston, MA, USA
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