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Launer BM, Ellis TA, Scarpato KR. A contemporary review: mpMRI in prostate cancer screening and diagnosis. Urol Oncol 2024:S1078-1439(24)00485-X. [PMID: 39129080 DOI: 10.1016/j.urolonc.2024.05.012] [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: 11/20/2023] [Revised: 01/29/2024] [Accepted: 05/18/2024] [Indexed: 08/13/2024]
Abstract
Prostate cancer (PCa) screening has evolved beyond PSA and digital rectal exam to include multiparametric prostate MRI (mpMRI). Incorporating this advanced imaging tool has further limited the well-established problem of overdiagnosis, aiding in the identification of higher grade, clinically significant cancers. For this reason, mpMRI has become an important part of the diagnostic pathway and is recommended across guidelines in biopsy naïve patients or for patients with prior negative biopsy. This contemporary review evaluates the most recent literature on the role of mpMRI in the screening and diagnosis of prostate cancer. Barriers to utilization of mpMRI still exist including variable access, high cost, and requisite expertise, encouraging evaluation of novel techniques such as biparametric MRI. Future screening and diagnostic practice patterns will undoubtedly evolve as our understanding of novel biomarkers and artificial intelligence improves.
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Affiliation(s)
- Bryn M Launer
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Taryn A Ellis
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kristen R Scarpato
- Department of Urology, Vanderbilt University Medical Center, Nashville, TN, United States.
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2
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Jager A, Oddens JR, Postema AW, Miclea RL, Schoots IG, Nooijen PGTA, van der Linden H, Barentsz JO, Heijmink SWTPJ, Wijkstra H, Mischi M, Turco S. Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis? Cancers (Basel) 2024; 16:2431. [PMID: 39001493 PMCID: PMC11240399 DOI: 10.3390/cancers16132431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 06/25/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.
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Affiliation(s)
- Auke Jager
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - Jorg R Oddens
- Department of Urology, Amsterdam UMC, University of Amsterdam, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Arnoud W Postema
- Leiden University Medical Center, Department of Urology, 2333 ZA Leiden, The Netherlands
| | - Razvan L Miclea
- Department of Radiology and Nuclear Imaging, Maastricht University Medical Centre+, 6229 HX Maastricht, The Netherlands
| | - Ivo G Schoots
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Peet G T A Nooijen
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Hans van der Linden
- Department of Pathology, Jeroen Bosch Hospital, 5223 GZ 's-Hertogenbosch, The Netherlands
| | - Jelle O Barentsz
- Department of Radiology, Radboud University Nijmegen Medical Center, 6525 GA Nijmegenfi, The Netherlands
| | - Stijn W T P J Heijmink
- Department of Radiology, Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Hessel Wijkstra
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Massimo Mischi
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
| | - Simona Turco
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
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Oh G, Moon Y, Moon WJ, Ye JC. Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements. Neuroimage 2024; 291:120571. [PMID: 38518829 DOI: 10.1016/j.neuroimage.2024.120571] [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: 11/23/2023] [Revised: 02/28/2024] [Accepted: 03/08/2024] [Indexed: 03/24/2024] Open
Abstract
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.
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Affiliation(s)
- Gyutaek Oh
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea
| | - Won-Jin Moon
- Department of Radiology, Konkuk University Medical Center, 120-1, Neungdong-ro, Gwangjin-gu, 05030, Seoul, Republic of Korea.
| | - Jong Chul Ye
- Kim Jaechul Graduate School of AI, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, 34141, Daejeon, Republic of Korea.
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Clemente A, Selva G, Berks M, Morrone F, Morrone AA, Aulisa MDC, Bliakharskaia E, De Nicola A, Tartaro A, Summers PE. Comparison of Early Contrast Enhancement Models in Ultrafast Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Prostate Cancer. Diagnostics (Basel) 2024; 14:870. [PMID: 38732285 PMCID: PMC11083228 DOI: 10.3390/diagnostics14090870] [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: 02/12/2024] [Revised: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 05/13/2024] Open
Abstract
Tofts models have failed to produce reliable quantitative markers for prostate cancer. We examined the differences between prostate zones and lesion PI-RADS categories and grade group (GG) using regions of interest drawn in tumor and normal-appearing tissue for a two-compartment uptake (2CU) model (including plasma volume (vp), plasma flow (Fp), permeability surface area product (PS), plasma mean transit time (MTTp), capillary transit time (Tc), extraction fraction (E), and transfer constant (Ktrans)) and exponential (amplitude (A), arrival time (t0), and enhancement rate (α)), sigmoidal (amplitude (A0), center time relative to arrival time (A1 - T0), and slope (A2)), and empirical mathematical models, and time to peak (TTP) parameters fitted to high temporal resolution (1.695 s) DCE-MRI data. In 25 patients with 35 PI-RADS category 3 or higher tumors, we found Fp and α differed between peripheral and transition zones. Parameters Fp, MTTp, Tc, E, α, A1 - T0, and A2 and TTP all showed associations with PI-RADS categories and with GG in the PZ when normal-appearing regions were included in the non-cancer GG. PS and Ktrans were not associated with any PI-RADS category or GG. This pilot study suggests early enhancement parameters derived from ultrafast DCE-MRI may become markers of prostate cancer.
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Affiliation(s)
- Alfredo Clemente
- Radiology Unit, Centro Medicina Nucleare N1, “Centro Morrone”, 81100 Caserta, Italy; (A.C.); (G.S.)
| | - Guerino Selva
- Radiology Unit, Centro Medicina Nucleare N1, “Centro Morrone”, 81100 Caserta, Italy; (A.C.); (G.S.)
| | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, University of Manchester, Manchester M13 9PL, UK;
| | - Federica Morrone
- Radiology Unit, Centro Radiologico Vega, “Centro Morrone”, 81100 Caserta, Italy; (F.M.); (A.A.M.)
| | | | | | | | - Andrea De Nicola
- Radiology Unit, SS. Annunziata Hospital, ASL Lanciano Vasto Chieti, 66100 Chieti, Italy;
| | - Armando Tartaro
- Department of Clinical, Oral Sciences and Biotechnology, University “G. d’Annunzio”, 66100 Chieti, Italy;
- MRI Unit, Santissima Trinità Hospital, ASL Pescara, 65026 Popoli, Italy
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Zhong J, Liu D, Yang Q, Ding J, Chen X. A Novel DNA Aptamer Probe Recognizing Castration Resistant Prostate Cancer in vitro and in vivo Based on Cell-SELEX. Drug Des Devel Ther 2024; 18:859-870. [PMID: 38524880 PMCID: PMC10959323 DOI: 10.2147/dddt.s444988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 03/09/2024] [Indexed: 03/26/2024] Open
Abstract
Background Early recognition of castration-resistant state is of significance for timely adjustment of treatment regimens and improvement of prognosis. Purpose This study aims to screen new aptamers CRda8 and CRda21 which recognize castration resistant prostate cancer (CRPC) cells with high affinity and specificity by SELEX technology. Methods The enrichment of specific aptamer candidates was monitored by flow cytometric analysis. The affinity and specificity of aptamer candidates were evaluated by flow cytometry and immunofluorescence assay. MR imaging of CRda21-conjugated polyethylene glycol (PEG)-Fe3O4 nanoparticles to CRPC was further explored in vivo. Results Both aptamers showed high specificity to target cells with dissociation constants in the nanomolar range, and did not recognize other tested cells. The staining of clinical tissue sections with fluorescent dye labeled aptamers showed that sections from CRPC exhibited stronger fluorescence while sections from benign prostatic hyperplasia and androgen dependent prostate cancer did not exhibit notable fluorescence. In vivo MRI demonstrated that CRda21-conjugated PEG-Fe3O4 had good affinity to CRPC and produced strong T2WI signal intensity reduction distinguished from peritumoral tissue. Conclusion The high affinity and specificity of CRda8 and CRda21 make the aptamer hold potential for early recognition of castration-resistant state and diagnosis of CRPC at the cellular level.
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Affiliation(s)
- Jinman Zhong
- Department of Radiology, The Second Affiliated Hospital, Xi’ an Jiaotong University, Xi’an, Shaanxi Province, 710004, People’s Republic of China
| | - Duoduo Liu
- Department of Radiology, The Second Affiliated Hospital, Xi’ an Jiaotong University, Xi’an, Shaanxi Province, 710004, People’s Republic of China
| | - Quanxin Yang
- Department of Radiology, The Second Affiliated Hospital, Xi’ an Jiaotong University, Xi’an, Shaanxi Province, 710004, People’s Republic of China
| | - Jianke Ding
- Department of Plastic and Reconstructive Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, Shaanxi Province, 710032, People’s Republic of China
| | - Xin Chen
- Department of Radiology, The Second Affiliated Hospital, Xi’ an Jiaotong University, Xi’an, Shaanxi Province, 710004, People’s Republic of China
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Pan J, Shao X, Liu H, Li Y, Wang Q. Image quality optimization: dynamic contrast-enhanced MRI of the abdomen at 3T using a continuously acquired radial golden-angle compressed sensing acquisition. Abdom Radiol (NY) 2024; 49:399-405. [PMID: 37792056 PMCID: PMC10830580 DOI: 10.1007/s00261-023-04035-4] [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: 03/20/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/05/2023]
Abstract
INTRODUCTION The image quality of continuously acquired free-breathing Dynamic Contrast-Enhanced (DCE) golden-angle radial Magnetic Resonance Imaging (MRI) of abdomen suffers from motion artifacts and motion-related blurring. We propose a scheme by minimizing patients' motion status from breathing as well as optimizing the acquiring parameters to improve image quality and diagnostic performance of DCE-MRI with Golden-Angle Radial Sparse Parallel (GRASP) sequence of abdomen. METHODS The optimization scheme follows two principles: (1) reduce the impact on images from unpredictable and irregulate motions during examination and (2) adjust the sequence parameters to increase the number of radial views in each partition. For the assessment of image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the severity of radial artifact, the degree of image sharpness, and a visual scoring of image quality with a 5-point scale were assessed. RESULTS A total of 64 patients were included in this study before (16 men, 14 women, age: 54.9 ± 17.0) and after (18 men, 16 women, age: 58.6 ± 12.6) the optimization scheme was performed. The results showed that the SNR values of right and left lobe of liver in both plain phase and arterial phase were significantly increased (All P < 0.001) after the GRASP sequence been optimized. Significant improvements in CNR values were observed in the arterial phase (All P < 0.05). The significant differences in scores at each phase for visual scoring of image quality, noise of the right and left lobe of liver, radial artifact, and sharpness indicating that the image quality was significantly improved after the optimization (All P < 0.001). CONCLUSION Our study demonstrated that the optimized scheme significantly improved the image quality of liver DCE-MRI with GRASP sequence both in plain and arterial phases. The optimized scheme of GRASP sequence could be a superior alternative to conventional approach for the assessment of liver.
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Affiliation(s)
- Jiangyang Pan
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China
| | - Xian Shao
- Department of Anesthesiology, The Fourth Hospital of Shijiazhuang, Shijiazhuang, 050000, Hebei, China
| | - Hui Liu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China.
| | - Yang Li
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China.
| | - Qi Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, China.
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Guljaš S, Dupan Krivdić Z, Drežnjak Madunić M, Šambić Penc M, Pavlović O, Krajina V, Pavoković D, Šmit Takač P, Štefančić M, Salha T. Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review. Diagnostics (Basel) 2023; 13:3488. [PMID: 37998624 PMCID: PMC10670922 DOI: 10.3390/diagnostics13223488] [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: 08/26/2023] [Revised: 10/30/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.
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Affiliation(s)
- Silva Guljaš
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (S.G.); (Z.D.K.)
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
| | - Zdravka Dupan Krivdić
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (S.G.); (Z.D.K.)
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
| | - Maja Drežnjak Madunić
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Oncology, University Hospital Centre, 31000 Osijek, Croatia
| | - Mirela Šambić Penc
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Oncology, University Hospital Centre, 31000 Osijek, Croatia
| | - Oliver Pavlović
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Vinko Krajina
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Deni Pavoković
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Urology, University Hospital Centre, 31000 Osijek, Croatia
| | - Petra Šmit Takač
- Clinical Department of Surgery, Osijek University Hospital Centre, 31000 Osijek, Croatia;
| | - Marin Štefančić
- Department of Radiology, National Memorial Hospital Vukovar, 32000 Vukovar, Croatia;
| | - Tamer Salha
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia; (M.D.M.); (M.Š.P.); (O.P.); (V.K.); (D.P.)
- Department of Teleradiology and Artificial Intelligence, Health Centre Osijek-Baranja County, 31000 Osijek, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
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Switlyk MD, Hopland A, Reitan E, Sivanesan S, Brennhovd B, Axcrona U, Hole KH. Multiparametric Magnetic Resonance Imaging of Penile Cancer: A Pictorial Review. Cancers (Basel) 2023; 15:5324. [PMID: 38001583 PMCID: PMC10670261 DOI: 10.3390/cancers15225324] [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: 10/04/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
The role of multiparametric magnetic resonance imaging (mpMRI) in assessing penile cancer is not well defined. However, this modality may be successfully applied for preoperative staging and patient selection; postoperative local and regional surveillance; and assessments of treatment response after oncological therapies. Previous studies have been mostly limited to a few small series evaluating the accuracy of MRI for the preoperative staging of penile cancer. This review discusses the principles of non-erectile mpMRI, including functional techniques and their applications in evaluating the male genital region, along with clinical protocols and technical considerations. The latest clinical classifications and guidelines are reviewed, focusing on imaging recommendations and discussing potential gaps and disadvantages. The development of functional MRI techniques and the extraction of quantitative parameters from these sequences enables the noninvasive assessment of phenotypic and genotypic tumor characteristics. The applications of advanced techniques in penile MRI are yet to be defined. There is a need for prospective trials and feasible multicenter trials due to the rarity of the disease, highlighting the importance of minimum technical requirements for MRI protocols, particularly image resolution, and finally determining the role of mpMRI in the assessment of penile cancer.
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Affiliation(s)
- Marta D. Switlyk
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (E.R.); (K.H.H.)
| | - Andreas Hopland
- Department of Urology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (A.H.); (S.S.); (B.B.)
| | - Edmund Reitan
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (E.R.); (K.H.H.)
| | - Shivanthe Sivanesan
- Department of Urology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (A.H.); (S.S.); (B.B.)
- Institute of Clinical Medicine (KlinMED), Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
| | - Bjørn Brennhovd
- Department of Urology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (A.H.); (S.S.); (B.B.)
| | - Ulrika Axcrona
- Department of Pathology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway;
| | - Knut H. Hole
- Department of Radiology, The Norwegian Radium Hospital, Oslo University Hospital, 0379 Oslo, Norway; (E.R.); (K.H.H.)
- Institute of Clinical Medicine (KlinMED), Faculty of Medicine, University of Oslo, 0318 Oslo, Norway
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Chang CB, Lin YC, Wong YC, Lin SN, Lin CY, Lin YH, Sheng TW, Yang LY, Wang LJ. Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Parameters Could Predict International Society of Urological Pathology Risk Groups of Prostate Cancers on Radical Prostatectomy. Life (Basel) 2023; 13:1944. [PMID: 37763347 PMCID: PMC10532885 DOI: 10.3390/life13091944] [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: 07/16/2023] [Revised: 08/22/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND The International Society of Urological Pathology (ISUP) grade and positive surgical margins (PSMs) after radical prostatectomy (RP) may reflect the prognosis of prostate cancer (PCa) patients. This study aimed to investigate whether DCE-MRI parameters (i.e., Ktrans, kep, and IAUC) could predict ISUP grade and PSMs after RP. METHOD Forty-five PCa patients underwent preoperative DCE-MRI. The clinical characteristics and DCE-MRI parameters of the 45 patients were compared between the low- and high-risk (i.e., ISUP grades III-V) groups and between patients with or without PSMs after RP. Multivariate logistic regression analysis was used to identify the significant predictors of placement in the high-risk group and PSMs. RESULTS The DCE parameter Ktrans-max was significantly higher in the high-risk group than in the low-risk group (p = 0.028) and was also a significant predictor of placement in the high-risk group (odds ratio [OR] = 1.032, 95% confidence interval [CI] = 1.005-1.060, p = 0.021). Patients with PSMs had significantly higher prostate-specific antigen (PSA) titers, positive biopsy core percentages, Ktrans-max, kep-median, and kep-max than others (all p < 0.05). Of these, positive biopsy core percentage (OR = 1.035, 95% CI = 1.003-1.068, p = 0.032) and kep-max (OR = 1.078, 95% CI = 1.012-1.148, p = 0.020) were significant predictors of PSMs. CONCLUSION Preoperative DCE-MRI parameters, specifically Ktrans-max and kep-max, could potentially serve as preoperative imaging biomarkers for postoperative PCa prognosis based on their predictability of PCa risk group and PSM on RP, respectively.
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Affiliation(s)
- Chun-Bi Chang
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
| | - Yu-Chun Lin
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
| | - Yon-Cheong Wong
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan 33305, Taiwan
| | - Shin-Nan Lin
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
| | | | - Yu-Han Lin
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
| | - Ting-Wen Sheng
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan 33305, Taiwan
| | - Lan-Yan Yang
- Biostatistics Unit of Clinical Trial Center, Chang Gung Memorial Hospital, Gueishan, Taoyuan 33305, Taiwan
| | - Li-Jen Wang
- Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Gueishan, Taoyuan 33305, Taiwan; (C.-B.C.)
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Medical Imaging and Intervention, New Taipei Municipal TuCheng Hospital, Chang Gung Memorial Hospital and Chang Gung University, Taoyuan 33305, Taiwan
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10
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Zhou X, Fan X, Chatterjee A, Yousuf A, Antic T, Oto A, Karczmar GS. Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard tofts model in the diagnosis of prostate cancer. Phys Eng Sci Med 2023; 46:1215-1226. [PMID: 37432557 DOI: 10.1007/s13246-023-01289-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 06/14/2023] [Indexed: 07/12/2023]
Abstract
The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast ([Formula: see text] and [Formula: see text]) and one slow ([Formula: see text] and [Formula: see text]) exchanging compartment, compared with the standard Tofts model parameters (Ktrans and kep). On average, prostate cancer had significantly higher values (p < 0.01) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.001) between Ktrans and [Formula: see text] for cancer, but weak correlation (r = 0.28, p < 0.05) between kep and [Formula: see text]. Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast [Formula: see text] had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM is useful for quantitative analysis of prostate DCE-MRI data and provides new information in the diagnosis of prostate cancer.
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Affiliation(s)
- Xueyan Zhou
- School of Technology, Harbin University, Harbin, China.
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA.
| | - Xiaobing Fan
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | | | - Ambereen Yousuf
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
| | - Tatjana Antic
- Department of Pathology, University of Chicago, Chicago, IL, 60637, USA
| | - Aytekin Oto
- Department of Radiology, University of Chicago, Chicago, IL, 60637, USA
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11
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Żurowska A, Pęksa R, Bieńkowski M, Skrobisz K, Sowa M, Matuszewski M, Biernat W, Szurowska E. Prostate Cancer and Its Mimics-A Pictorial Review. Cancers (Basel) 2023; 15:3682. [PMID: 37509343 PMCID: PMC10378330 DOI: 10.3390/cancers15143682] [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: 05/03/2023] [Revised: 06/24/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Multiparametric prostate MRI (mpMRI) is gaining wider recommendations for diagnosing and following up on prostate cancer. However, despite the high accuracy of mpMRI, false positive and false negative results are reported. Some of these may be related to normal anatomic structures, benign lesions that may mimic cancer, or poor-quality images that hamper interpretation. The aim of this review is to discuss common potential pitfalls in the interpretation of mpMRI. METHODS mpMRI of the prostates was performed on 3T MRI scanners (Philips Achieva or Siemens Magnetom Vida) according to European Society of Urogenital Radiology (ESUR) guidelines and technical requirements. RESULTS This pictorial review discusses normal anatomical structures such as the anterior fibromuscular stroma, periprostatic venous plexus, central zone, and benign conditions such as benign prostate hyperplasia (BPH), post-biopsy hemorrhage, prostatitis, and abscess that may imitate prostate cancer, as well as the appearance of prostate cancer occurring in these locations. Furthermore, suggestions on how to avoid these pitfalls are provided, and the impact of image quality is also discussed. CONCLUSIONS In an era of accelerating prostate mpMRI and high demand for high-quality interpretation of the scans, radiologists should be aware of these potential pitfalls to improve their diagnostic accuracy.
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Affiliation(s)
- Anna Żurowska
- Second Department of Radiology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Rafał Pęksa
- Department of Pathomorphology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Michał Bieńkowski
- Department of Pathomorphology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Katarzyna Skrobisz
- Department of Radiology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Marek Sowa
- Department of Urology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Marcin Matuszewski
- Department of Urology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Wojciech Biernat
- Department of Pathomorphology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
| | - Edyta Szurowska
- Second Department of Radiology, Medical University of Gdańsk, 80-214 Gdańsk, Poland
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12
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Hellstern M, Martinez C, Wallenhorst C, Beyersdorff D, Lüdemann L, Grimm MO, Teichgräber U, Franiel T. Optimal length and temporal resolution of dynamic contrast-enhanced MR imaging for the differentiation between prostate cancer and normal peripheral zone tissue. PLoS One 2023; 18:e0287651. [PMID: 37352312 PMCID: PMC10289347 DOI: 10.1371/journal.pone.0287651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 06/12/2023] [Indexed: 06/25/2023] Open
Abstract
The value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the detection of prostate cancer is controversial. There are currently insufficient peer reviewed published data or expert consensus to support routine adoption of DCE-MRI for clinical use. Thus, the objective of this study was to explore the optimal temporal resolution and measurement length for DCE-MRI to differentiate cancerous from normal prostate tissue of the peripheral zone of the prostate by non-parametric MRI analysis and to compare with a quantitative MRI analysis. Predictors of interest were onset time, relative signal intensity (RSI), wash-in slope, peak enhancement, wash-out and wash-out slope determined from non-parametric characterisation of DCE-MRI intensity-time profiles. The discriminatory power was estimated from C-statistics based on cross validation. We analyzed 54 patients with 97 prostate tissue specimens (47 prostate cancer, 50 normal prostate tissue) of the peripheral zone, mean age 63.8 years, mean prostate-specific antigen 18.9 ng/mL and mean of 10.5 days between MRI and total prostatectomy. When comparing prostate cancer tissue with normal prostate tissue, median RSI was 422% vs 330%, and wash-in slope 0.870 vs 0.539. The peak enhancement of 67 vs 42 was higher with prostate cancer tissue, while wash-out (-30% vs -23%) and wash-out slope (-0.037 vs -0.029) were lower, and the onset time (32 seconds) was comparable. The optimal C-statistics was 0.743 for temporal resolution of 8.0 seconds and measurement length of 2.5 minutes compared with 0.656 derived from a quantitative MRI analysis. This study provides evidence that the use of a non-parametric approach instead of a more established parametric approach resulted in greater precision to differentiate cancerous from normal prostate tissue of the peripheral zone of the prostate.
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Affiliation(s)
- Marius Hellstern
- Bürgerhospital und Clementin Kinderhospital gGmbH, Frankfurt am Main, Germany
| | - Carlos Martinez
- Institute for Epidemiology, Statistics and Informatics GmbH, Frankfurt am Main, Germany
| | | | - Dirk Beyersdorff
- Department of Diagnostic and Interventional Radiology, University Hospital Hamburg Eppendorf, Hamburg, Germany
| | - Lutz Lüdemann
- Department of Medical Physics, Essen University Hospital, Essen, Germany
| | - Marc-Oliver Grimm
- Klinik und Poliklinik für Urologie Universitätsklinikum Jena, Jena, Germany
| | - Ulf Teichgräber
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Jena, Jena, Germany
| | - Tobias Franiel
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Jena, Jena, Germany
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13
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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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14
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Ferda J, Frölich M, Ferdová E, Heidenreich F, Charvát R, Mírka H. Neovascularization, vascular mimicry and molecular exchange: The imaging of tumorous tissue aggressiveness based on tissue perfusion. Eur J Radiol 2023; 163:110797. [PMID: 37018901 DOI: 10.1016/j.ejrad.2023.110797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/19/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023]
Abstract
Angiogenesis in healthy tissue and within malignant tumors differs on many levels, which may partly be explained by vascular mimicry formation resulting in altered contrast material or different radiopharmaceuticals distributions. Failed remodulation results in changes in the molecular exchange through the capillary wall and those consequences affect the behavior of contrast agents and radiopharmaceuticals. One of the most indicative signs of malignant tissue is the increased permeability and the faster molecular exchange that occurs between the extracellular and intravascular spaces. Dynamic imaging can help to assess the changed microenvironment. The fast-distribution of molecules reflects newly developed conditions in blood-flow redistribution inside a tumor and within the affected organ during the early stages of tumor formation. Tumor development, as well as aggressiveness, can be assessed based on the change to the vascular bed development, the level of molecular exchange within the tissue, and/or indicative distribution within the organ. The study of the vascular network organization and its impact on the distribution of molecules is important to our understanding of the image pattern in several imaging methods, which in turn influences our interpretation of the findings. A hybrid imaging approach (including PET/MRI) allows the quantification of vascularization and/or its pathophysiological impressions in structural and metabolic images. It might optimize the evaluation of the pretreatment imaging, as well as help assess the effect of therapy targeting neovascularization; antiVEGF drugs and embolization-based therapies, for example.
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Affiliation(s)
- Jiří Ferda
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic.
| | - Matthias Frölich
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic; Klinik für Radiologie und Nuklearmedizin, Universitäts Klinikum Mannheim
| | - Eva Ferdová
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic
| | - Filip Heidenreich
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic
| | - Radim Charvát
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic
| | - Hynek Mírka
- Department of the Imaging, University Hospital Pilsen and Charles University Medical Faculty in Pilsen, Czech Republic
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15
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Akin O, Woo S, Oto A, Allen BC, Avery R, Barker SJ, Gerena M, Halpern DJ, Gettle LM, Rosenthal SA, Taneja SS, Turkbey B, Whitworth P, Nikolaidis P. ACR Appropriateness Criteria® Pretreatment Detection, Surveillance, and Staging of Prostate Cancer: 2022 Update. J Am Coll Radiol 2023; 20:S187-S210. [PMID: 37236742 DOI: 10.1016/j.jacr.2023.02.010] [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: 02/21/2023] [Accepted: 02/27/2023] [Indexed: 05/28/2023]
Abstract
Prostate cancer is second leading cause of death from malignancy after lung cancer in American men. The primary goal during pretreatment evaluation of prostate cancer is disease detection, localization, establishing disease extent (both local and distant), and evaluating aggressiveness, which are the driving factors of patient outcomes such as recurrence and survival. Prostate cancer is typically diagnosed after the recognizing elevated serum prostate-specific antigen level or abnormal digital rectal examination. Tissue diagnosis is obtained by transrectal ultrasound-guided biopsy or MRI-targeted biopsy, commonly with multiparametric MRI without or with intravenous contrast, which has recently been established as standard of care for detecting, localizing, and assessing local extent of prostate cancer. Although bone scintigraphy and CT are still typically used to detect bone and nodal metastases in patients with intermediate- or high-risk prostate cancer, novel advanced imaging modalities including prostatespecific membrane antigen PET/CT and whole-body MRI are being more frequently utilized for this purpose with improved detection rates. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
- Oguz Akin
- Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Sungmin Woo
- Research Author, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Aytekin Oto
- Panel Chair, University of Chicago, Chicago, Illinois
| | - Brian C Allen
- Panel Vice-Chair, Duke University Medical Center, Durham, North Carolina
| | - Ryan Avery
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Commission on Nuclear Medicine and Molecular Imaging
| | - Samantha J Barker
- University of Minnesota, Minneapolis, Minnesota; Director of Ultrasound M Health Fairview
| | | | - David J Halpern
- Duke University Medical Center, Durham, North Carolina, Primary care physician
| | | | - Seth A Rosenthal
- Sutter Medical Group, Sacramento, California; Commission on Radiation Oncology; Member, RTOG Foundation Board of Directors
| | - Samir S Taneja
- NYU Clinical Cancer Center, New York, New York; American Urological Association
| | - Baris Turkbey
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Pat Whitworth
- Thomas F. Frist, Jr College of Medicine, Belmont University, Nashville, Tennessee
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16
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Gibala S, Obuchowicz R, Lasek J, Schneider Z, Piorkowski A, Pociask E, Nurzynska K. Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels. J Clin Med 2023; 12:jcm12082836. [PMID: 37109173 PMCID: PMC10146387 DOI: 10.3390/jcm12082836] [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: 03/17/2023] [Revised: 04/06/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Prostate cancer, which is associated with gland biology and also with environmental risks, is a serious clinical problem in the male population worldwide. Important progress has been made in the diagnostic and clinical setups designed for the detection of prostate cancer, with a multiparametric magnetic resonance diagnostic process based on the PIRADS protocol playing a key role. This method relies on image evaluation by an imaging specialist. The medical community has expressed its desire for image analysis techniques that can detect important image features that may indicate cancer risk. METHODS Anonymized scans of 41 patients with laboratory diagnosed PSA levels who were routinely scanned for prostate cancer were used. The peripheral and central zones of the prostate were depicted manually with demarcation of suspected tumor foci under medical supervision. More than 7000 textural features in the marked regions were calculated using MaZda software. Then, these 7000 features were used to perform region parameterization. Statistical analyses were performed to find correlations with PSA-level-based diagnosis that might be used to distinguish suspected (different) lesions. Further multiparametrical analysis using MIL-SVM machine learning was used to obtain greater accuracy. RESULTS Multiparametric classification using MIL-SVM allowed us to reach 92% accuracy. CONCLUSIONS There is an important correlation between the textural parameters of MRI prostate images made using the PIRADS MR protocol with PSA levels > 4 mg/mL. The correlations found express dependence between image features with high cancer markers and hence the cancer risk.
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Affiliation(s)
- Sebastian Gibala
- Urology Department, Ultragen Medical Center, 31-572 Krakow, Poland
| | - Rafal Obuchowicz
- Department of Diagnostic Imaging, Jagiellonian University Medical College, 31-501 Krakow, Poland
| | - Julia Lasek
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Zofia Schneider
- Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Adam Piorkowski
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Elżbieta Pociask
- Department of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, 30-059 Krakow, Poland
| | - Karolina Nurzynska
- Department of Algorithmics and Software, Silesian University of Technology, 44-100 Gliwice, Poland
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17
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Asif A, Nathan A, Ng A, Khetrapal P, Chan VWS, Giganti F, Allen C, Freeman A, Punwani S, Lorgelly P, Clarke CS, Brew-Graves C, Muirhead N, Emberton M, Agarwal R, Takwoingi Y, Deeks JJ, Moore CM, Kasivisvanathan V. Comparing biparametric to multiparametric MRI in the diagnosis of clinically significant prostate cancer in biopsy-naive men (PRIME): a prospective, international, multicentre, non-inferiority within-patient, diagnostic yield trial protocol. BMJ Open 2023; 13:e070280. [PMID: 37019486 PMCID: PMC10083803 DOI: 10.1136/bmjopen-2022-070280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
INTRODUCTION Prostate MRI is a well-established tool for the diagnostic work-up for men with suspected prostate cancer (PCa). Current recommendations advocate the use of multiparametric MRI (mpMRI), which is composed of three sequences: T2-weighted sequence (T2W), diffusion-weighted sequence (DWI) and dynamic contrast-enhanced sequence (DCE). Prior studies suggest that a biparametric MRI (bpMRI) approach, omitting the DCE sequences, may not compromise clinically significant cancer detection, though there are limitations to these studies, and it is not known how this may affect treatment eligibility. A bpMRI approach will reduce scanning time, may be more cost-effective and, at a population level, will allow more men to gain access to an MRI than an mpMRI approach. METHODS Prostate Imaging Using MRI±Contrast Enhancement (PRIME) is a prospective, international, multicentre, within-patient diagnostic yield trial assessing whether bpMRI is non-inferior to mpMRI in the diagnosis of clinically significant PCa. Patients will undergo the full mpMRI scan. Radiologists will be blinded to the DCE and will initially report the MRI using only the bpMRI (T2W and DWI) sequences. They will then be unblinded to the DCE sequence and will then re-report the MRI using the mpMRI sequences (T2W, DWI and DCE). Men with suspicious lesions on either bpMRI or mpMRI will undergo prostate biopsy. The main inclusion criteria are men with suspected PCa, with a serum PSA of ≤20 ng/mL and without prior prostate biopsy. The primary outcome is the proportion of men with clinically significant PCa detected (Gleason score ≥3+4 or Gleason grade group ≥2). A sample size of at least 500 patients is required. Key secondary outcomes include the proportion of clinically insignificant PCa detected and treatment decision. ETHICS AND DISSEMINATION Ethical approval was obtained from the National Research Ethics Committee West Midlands, Nottingham (21/WM/0091). Results of this trial will be disseminated through peer-reviewed publications. Participants and relevant patient support groups will be informed about the results of the trial. TRIAL REGISTRATION NUMBER NCT04571840.
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Affiliation(s)
- Aqua Asif
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Arjun Nathan
- Division of Surgery and Interventional Science, University College London, London, UK
- Clinical Effectiveness Unit, Royal College of Surgeons of England, London, UK
| | - Alexander Ng
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Pramit Khetrapal
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, Whipps Cross University Hospital, London, UK
| | - Vinson Wai-Shun Chan
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Francesco Giganti
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Clare Allen
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Alex Freeman
- Department of Histopathology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Shonit Punwani
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
- Centre for Medical Imaging, University College London, London, UK
| | - Paula Lorgelly
- Institute of Epidemiology and Health Care, University College London, London, UK
- School of Population Health, The University of Auckland, Auckland, New Zealand
| | - Caroline S Clarke
- Research Department of Primary Care and Population Health, University College London, London, UK
| | - Chris Brew-Graves
- National Cancer Imaging Translational Accelerator, University College London, London, UK
| | - Nicola Muirhead
- National Cancer Imaging Translational Accelerator, University College London, London, UK
| | - Mark Emberton
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Ridhi Agarwal
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Caroline M Moore
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Veeru Kasivisvanathan
- Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
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18
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Eraky AM. Radiological Biomarkers for Brain Metastases Prognosis: Quantitative Magnetic Resonance Imaging (MRI) Modalities As Non-invasive Biomarkers for the Effect of Radiotherapy. Cureus 2023; 15:e38353. [PMID: 37266043 PMCID: PMC10229388 DOI: 10.7759/cureus.38353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
Radiotherapy effect is achieved by its ability to cause DNA damage and induce apoptosis. In contrast, radiation can induce tumor cells' proliferation, invasiveness, and epithelial-mesenchymal transition (EMT). Besides developing radioresistance, this paradoxical effect of radiotherapy is considered a challenging problem in the field of radiotherapy. This highlights the importance of developing new modalities to diagnose radioresistance early to avoid any unnecessary exposure to radiation and differentiate between metastases recurrence versus post-radiation changes. Quantitative magnetic resonance imaging (MRI) techniques including diffusion-weighted imaging (DWI), dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast-enhanced (DCE) represent potential biomarkers to diagnose metastases recurrence and radioresistance. In this review, we will focus on recent studies discussing the possibility of using DWI, DSC, ASL, and DCE to diagnose radioresistance and recurrence in patients with brain metastases.
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Affiliation(s)
- Akram M Eraky
- Neurological Surgery, Medical College of Wisconsin, Milwaukee, USA
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Chitalia R, Miliotis M, Jahani N, Tastsoglou S, McDonald ES, Belenky V, Cohen EA, Newitt D, Van't Veer LJ, Esserman L, Hylton N, DeMichele A, Hatzigeorgiou A, Kontos D. Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy. COMMUNICATIONS MEDICINE 2023; 3:46. [PMID: 36997615 PMCID: PMC10063641 DOI: 10.1038/s43856-023-00273-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 03/10/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS). METHODS A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components. RESULTS We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002). CONCLUSIONS These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.
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Affiliation(s)
- Rhea Chitalia
- Department of Bioengineering, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Marios Miliotis
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Nariman Jahani
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Spyros Tastsoglou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Elizabeth S McDonald
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Vivian Belenky
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Eric A Cohen
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - David Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Laura J Van't Veer
- Department of Surgery and Oncology, University of California, San Francisco, USA
| | - Laura Esserman
- Department of Surgery and Oncology, University of California, San Francisco, USA
| | - Nola Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, USA
| | - Angela DeMichele
- Department of Medicine, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Artemis Hatzigeorgiou
- Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
- DIANA-Lab, Hellenic Pasteur Institute, Athens, Greece
| | - Despina Kontos
- Department of Radiology, Division of Hematology/Oncology, University of Pennsylvania, Perelman School of Medicine 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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20
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Zhang J, Xu L, Zhang G, Zhang X, Bai X, Sun H, Jin Z. Effects of dynamic contrast enhancement on transition zone prostate cancer in Prostate Imaging Reporting and Data System Version 2.1. Radiol Oncol 2023; 57:42-50. [PMID: 36655324 PMCID: PMC10039479 DOI: 10.2478/raon-2023-0007] [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: 10/09/2022] [Accepted: 11/18/2022] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND The aim of the study was to analyse the effects of dynamic contrast enhanced (DCE)-MRI on transitional-zone prostate cancer (tzPCa) and clinically significant transitional-zone prostate cancer (cs-tzPCa) in Prostate Imaging Reporting and Data System (PI-RADS) Version 2.1. PATIENTS AND METHODS The diagnostic efficiencies of T2-weighted imaging (T2WI) + diffusion-weighted imaging (DWI), T2WI + dynamic contrast-enhancement (DCE), and T2WI + DWI + DCE in tzPCa and cs-tzPCa were compared using the score of ≥ 4 as the positive threshold and prostate biopsy as the reference standard. RESULTS A total of 425 prostate cases were included in the study: 203 cases in the tzPCa group, and 146 in the cs-tzPCa group. The three sequence combinations had the similar areas under the curves in diagnosing tzPCa and cs-tzPCa (all P < 0.05). The sensitivity of T2WI + DCE and T2WI + DWI + DCE (84.7% and 85.7% for tzPCa; 88.4% and 89.7% for cs-tzPCa, respectively) in diagnosing tzPCa and cs-tzPCa was significantly greater than that of T2WI + DWI (79.3% for tzPCa; 82.9% for cs-tzPCa). The specificity of T2WI + DWI (86.5% for tzPCa; 74.9% for cs-tzPCa) were significantly greater than those of T2WI + DCE and T2WI + DWI + DCE (68.0% and 68.5% for tzPCa; 59.1% and 59.5% for cs-tzPCa, respectively) (all P > 0.05). The diagnostic efficacies of T2WI + DCE and T2WI + DWI + DCE had no significant differences (all P < 0.05). CONCLUSIONS DCE can improve the sensitivity of diagnosis for tzPCa and cs-tzPCa, and it is useful for small PCa lesion diagnosis.
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Affiliation(s)
- Jiahui Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Lili Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Gumuyang Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xiaoxiao Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Xin Bai
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Sun
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- National Center for Quality Control of Radiology, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- National Center for Quality Control of Radiology, Beijing, China
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21
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Zhou X, Fan X, Chatterjee A, Yousuf A, Antic T, Oto A, Karczmar GS. Parametric maps of spatial two-tissue compartment model for prostate dynamic contrast enhanced MRI - comparison with the standard Tofts model in the diagnosis of prostate cancer. RESEARCH SQUARE 2023:rs.3.rs-2539644. [PMID: 36798227 PMCID: PMC9934750 DOI: 10.21203/rs.3.rs-2539644/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
The spatial two-tissue compartment model (2TCM) was used to analyze prostate dynamic contrast enhanced (DCE) MRI data and compared with the standard Tofts model. A total of 29 patients with biopsy-confirmed prostate cancer were included in this IRB-approved study. MRI data were acquired on a Philips Achieva 3T-TX scanner. After T2-weighted and diffusion-weighted imaging, DCE data using 3D T1-FFE mDIXON sequence were acquired pre- and post-contrast media injection (0.1 mmol/kg Multihance) for 60 dynamic scans with temporal resolution of 8.3 s/image. The 2TCM has one fast (K 1 trans and k 1 ep ) and one slow (K 2 trans and k 2 ep ) exchanging compartment, compared with the standard Tofts model parameters (K trans and k ep ). On average, prostate cancer had significantly higher values (p < 0.007) than normal prostate tissue for all calculated parameters. There was a strong correlation (r = 0.94, p < 0.0001) between K trans and K 1 trans for cancer, but weak correlation (r = 0.28, p < 0.05) between k ep and k 1 ep . Average root-mean-square error (RMSE) in fits from the 2TCM was significantly smaller (p < 0.001) than the RMSE in fits from the Tofts model. Receiver operating characteristic (ROC) analysis showed that fast K 1 trans had the highest area under the curve (AUC) than any other individual parameter. The combined four parameters from the 2TCM had a considerably higher AUC value than the combined two parameters from the Tofts model. The 2TCM may be useful for quantitative analysis of prostate DCE-MRI data and may provide new information in the diagnosis of prostate cancer.
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22
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Alley S, Jackson E, Olivié D, Van der Heide UA, Ménard C, Kadoury S. Effect of magnetic resonance imaging pre-processing on the performance of model-based prostate tumor probability mapping. Phys Med Biol 2022; 67. [PMID: 36223780 DOI: 10.1088/1361-6560/ac99b4] [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: 03/10/2022] [Accepted: 10/12/2022] [Indexed: 11/07/2022]
Abstract
Objective. Multi-parametric magnetic resonance imaging (mpMRI) has become an important tool for the detection of prostate cancer in the past two decades. Despite the high sensitivity of MRI for tissue characterization, it often suffers from a lack of specificity. Several well-established pre-processing tools are publicly available for improving image quality and removing both intra- and inter-patient variability in order to increase the diagnostic accuracy of MRI. To date, most of these pre-processing tools have largely been assessed individually. In this study we present a systematic evaluation of a multi-step mpMRI pre-processing pipeline to automate tumor localization within the prostate using a previously trained model.Approach. The study was conducted on 31 treatment-naïve prostate cancer patients with a PI-RADS-v2 compliant mpMRI examination. Multiple methods were compared for each pre-processing step: (1) bias field correction, (2) normalization, and (3) deformable multi-modal registration. Optimal parameter values were estimated for each step on the basis of relevant individual metrics. Tumor localization was then carried out via a model-based approach that takes both mpMRI and prior clinical knowledge features as input. A sequential optimization approach was adopted for determining the optimal parameters and techniques in each step of the pipeline.Main results. The application of bias field correction alone increased the accuracy of tumor localization (area under the curve (AUC) = 0.77;p-value = 0.004) over unprocessed data (AUC = 0.74). Adding normalization to the pre-processing pipeline further improved diagnostic accuracy of the model to an AUC of 0.85 (p-value = 0.000 12). Multi-modal registration of apparent diffusion coefficient images to T2-weighted images improved the alignment of tumor locations in all but one patient, resulting in a slight decrease in accuracy (AUC = 0.84;p-value = 0.30).Significance. Overall, our findings suggest that the combined effect of multiple pre-processing steps with optimal values has the ability to improve the quantitative classification of prostate cancer using mpMRI. Clinical trials: NCT03378856 and NCT03367702.
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Affiliation(s)
| | - Edward Jackson
- The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Damien Olivié
- Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | | | - Cynthia Ménard
- Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
| | - Samuel Kadoury
- Polytechnique Montréal, Montréal, Québec, Canada.,Centre Hospitalier de l'Université de Montréal, Montréal, Québec, Canada
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23
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Knight AS, Sharma P, de Riese WTW. MRI determined prostate volume and the incidence of prostate cancer on MRI-fusion biopsy: a systemic review of reported data for the last 20 years. Int Urol Nephrol 2022; 54:3047-3054. [PMID: 36040649 DOI: 10.1007/s11255-022-03351-w] [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: 07/13/2022] [Accepted: 08/20/2022] [Indexed: 10/14/2022]
Abstract
PURPOSE Magnetic resonance imaging (MRI) is a precise, systemic and advantageous imaging technique when compared to transrectal ultrasound (TRUS) which is very operator dependent. The negative correlation between prostate volume and the incidence of prostate cancer (PCa) obtained by TRUS biopsy has been well documented in the literature. The purpose of this systemic review is analyzing the reported MRI-fusion study results on prostate biopsies regarding any correlation between prostate volume and the incidence of PCa. METHODS After defining the inclusion and exclusion criteria an in-depth review were performed between 01.01.2000 and 02.08.2022 using the PubMed database and applying the "PRISMA" guidelines. RESULTS Twelve studies qualified, and all showed an inverse/negative relationship between prostate volume and incidence of PCa. Sample sizes ranged from 33 to 2767 patients in single and multi-institutional studies. All studies showed a statistically significant inverse relationship with a p value < 0.05. The graph summarizing all of studies and using Fisher's method revealed a highly significant combined p level of 0.00001. Additionally, not one single study was found showing the contrary (a positive correlation between prostate size and the incidence of PCa). CONCLUSION To our knowledge, this is the first systemic review of reported MRI-Fusion data on the incidence of PCa in correlation with prostate volume. This MRI review confirms previous TRUS-biopsy studies which demonstrated an inverse relationship between prostate volume and the incidence of PCa, and thus further supports the hypothesis that large prostates size may be protective against PCa when compared to smaller prostates.
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Affiliation(s)
- Andrew S Knight
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA
| | - Pranav Sharma
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA
| | - Werner T W de Riese
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, 3601-4th Street STOP 7260, Lubbock, TX, 79430-7260, USA.
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24
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Eresen A, Sun C, Zhou K, Shangguan J, Wang B, Pan L, Hu S, Ma Q, Yang J, Zhang Z, Yaghmai V. Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI. Acad Radiol 2022; 29:1378-1386. [PMID: 34933803 PMCID: PMC10029937 DOI: 10.1016/j.acra.2021.11.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE AND OBJECTIVES Irreversible electroporation (IRE) is a promising non-thermal ablation technique for the treatment of patients with hepatocellular carcinoma. Early differentiation of the IRE zone from surrounding reversibly electroporated (RE) penumbra is vital for the evaluation of treatment response. In this study, an advanced statistical learning framework was developed by evaluating standard MRI data to differentiate IRE ablation zones, and to correlate with histological tumor biomarkers. MATERIALS AND METHODS Fourteen rabbits with VX2 liver tumors were scanned following IRE ablation and forty-six features were extracted from T1w and T2w MRI. Following identification of key imaging variables through two-step feature analysis, multivariable classification and regression models were generated for differentiation of IRE ablation zones, and correlation with histological markers reflecting viable tumor cells, microvessel density, and apoptosis rate. The performance of the multivariable models was assessed by measuring accuracy, receiver operating characteristics curve analysis, and Spearman correlation coefficients. RESULTS The classifiers integrating four radiomics features of T1w, T2w, and T1w+T2w MRI data distinguished IRE from RE zones with an accuracy of 97%, 80%, and 97%, respectively. Also, pixelwise classification models of T1w, T2w, and T1w+T2w MRI labeled each voxel with an accuracy of 82.8%, 66.5%, and 82.9%, respectively. Regression models obtained a strong correlation with behavior of viable tumor cells (0.62 ≤ r2 ≤ 0.85, p < 0.01), apoptosis (0.40 ≤ r2 ≤ 0.82, p < 0.01), and microvessel density (0.48 ≤ r2 ≤ 0.58, p < 0.01). CONCLUSION MRI radiomics features provide descriptive power for early differentiation of IRE and RE zones while observing strong correlations among multivariable MRI regression models and histological tumor biomarkers.
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Affiliation(s)
- Aydin Eresen
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Chong Sun
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Orthopedics, Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Kang Zhou
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Peking Union Medical College Hospital, Beijing, China
| | - Junjie Shangguan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Bin Wang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Guangzhou, China
| | - Liang Pan
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, Third Affiliated Hospital of Suzhou University, Changzhou, Jiangsu, China
| | - Su Hu
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiology, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
| | - Quanhong Ma
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California
| | - Jia Yang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Zhuoli Zhang
- Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; Department of Radiological Sciences, University of California Irvine, Irvine, California; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California
| | - Vahid Yaghmai
- Department of Radiological Sciences, University of California Irvine, Irvine, California; Chao Family Comprehensive Cancer Center, University of California Irvine, Irvine, California.
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25
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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26
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Comparison between biparametric and multiparametric MRI diagnosis strategy for prostate cancer in the peripheral zone using PI-RADS version 2.1. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:2905-2916. [PMID: 35622121 DOI: 10.1007/s00261-022-03553-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 05/04/2022] [Accepted: 05/05/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To compare and analyse the diagnostic value of PI-RADS v2.1 when used with biparametric MRI (bpMRI) versus multiparametric MRI (mpMRI), DWI versus T2WI to detect peripheral-zone prostate cancer (pzPCa) and clinically significant peripheral-zone prostate cancer (cs-pzPCa). METHODS The diagnostic efficiencies of mpMRI and bpMRI as well as DWI and T2WI in pzPCa and cs-pzPCa were compared using a PI-RADS score of ≥ 4 as the positive threshold and prostate biopsy and radical prostatectomy as the reference standards. RESULTS A total of 307 prostate cases were included in the study, including 142 in the non-pzPCa group, 165 in the pzPCa group, and 130 in the cs-pzPCa group. The AUCs of mpMRI and bpMRI were 0.717 and 0.733 (P = 0.317), respectively, for the diagnosis of pzPCa (sensitivities: 89.1% and 81.8%; specificities: 54.2% and 64.8%, both P < 0.001) and 0.594 and 0.602 (P = 0.756), respectively, for the diagnosis of cs-pzPCa (sensitivities: 93.1% and 86.2%, P = 0.004; specificities: 25.7% and 34.3%, P = 0.250). The AUCs of DWI and T2WI were 0.733 and 0.749 (P = 0.308), respectively, for the diagnosis of pzPCa (sensitivities: 81.8% and 84.2%; specificities: 64.8% and 66.2%, both P > 0.05) and 0.602 and 0.581 (P = 0.371), respectively, for the diagnosis of cs-pzPCa (sensitivities: 86.2% and 87.7%; specificities: 34.3% and 28.6%, both P > 0.05). CONCLUSION mpMRI and bpMRI as well as DWI and T2WI using PI-RADS v2.1 exhibited similar diagnostic efficiency in pzPCa and cs-pzPCa.
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27
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Mazaheri Y, Kim N, Lakhman Y, Jafari R, Vargas A, Otazo R. Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters. NMR IN BIOMEDICINE 2022; 35:e4718. [PMID: 35226774 PMCID: PMC9203940 DOI: 10.1002/nbm.4718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/17/2022] [Accepted: 02/22/2022] [Indexed: 06/14/2023]
Abstract
The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.
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Affiliation(s)
- Yousef Mazaheri
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nathanael Kim
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Yulia Lakhman
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ramin Jafari
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Ricardo Otazo
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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28
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Guljaš S, Benšić M, Krivdić Dupan Z, Pavlović O, Krajina V, Pavoković D, Šmit Takač P, Hranić M, Salha T. Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate—Can We Make Better Use of It? Tomography 2022; 8:1509-1521. [PMID: 35736872 PMCID: PMC9231365 DOI: 10.3390/tomography8030124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/18/2022] [Accepted: 06/04/2022] [Indexed: 11/16/2022] Open
Abstract
We sought to investigate whether quantitative parameters from a dynamic contrast-enhanced study can be used to differentiate cancer from normal tissue and to determine a cut-off value of specific parameters that can predict malignancy more accurately, compared to the obturator internus muscle as a reference tissue. This retrospective study included 56 patients with biopsy proven prostate cancer (PCa) after multiparametric magnetic resonance imaging (mpMRI), with a total of 70 lesions; 39 were located in the peripheral zone, and 31 in the transition zone. The quantitative parameters for all patients were calculated in the detected lesion, morphologically normal prostate tissue and the obturator internus muscle. Increase in the Ktrans value was determined in lesion-to-muscle ratio by 3.974368, which is a cut-off value to differentiate between prostate cancer and normal prostate tissue, with specificity of 72.86% and sensitivity of 91.43%. We introduced a model to detect prostate cancer that combines Ktrans lesion-to-muscle ratio value and iAUC lesion-to-muscle ratio value, which is of higher accuracy compared to individual variables. Based on this model, we identified the optimal cut-off value with 100% sensitivity and 64.28% specificity. The use of quantitative DCE pharmacokinetic parameters compared to the obturator internus muscle as reference tissue leads to higher diagnostic accuracy for prostate cancer detection.
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Affiliation(s)
- Silva Guljaš
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (Z.K.D.); (M.H.)
- Correspondence:
| | - Mirta Benšić
- Department of Mathematics, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia;
| | - Zdravka Krivdić Dupan
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (Z.K.D.); (M.H.)
- Department of Radiology, Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia;
| | - Oliver Pavlović
- Department of Urology, University Hospital Centre Osijek, 31000 Osijek, Croatia; (O.P.); (V.K.); (D.P.)
| | - Vinko Krajina
- Department of Urology, University Hospital Centre Osijek, 31000 Osijek, Croatia; (O.P.); (V.K.); (D.P.)
| | - Deni Pavoković
- Department of Urology, University Hospital Centre Osijek, 31000 Osijek, Croatia; (O.P.); (V.K.); (D.P.)
| | - Petra Šmit Takač
- Clinical Department of Surgery, Osijek University Hospital Centre, 31000 Osijek, Croatia;
| | - Matija Hranić
- Clinical Department of Radiology, University Hospital Centre, 31000 Osijek, Croatia; (Z.K.D.); (M.H.)
| | - Tamer Salha
- Department of Radiology, Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia;
- Department of Teleradiology and Artificial Intelligence, Health Centre Osijek-Baranja County, 31000 Osijek, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
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Fan X, Chatterjee A, Pittman JM, Yousuf A, Antic T, Karczmar GS, Oto A. Effectiveness of Dynamic Contrast Enhanced MRI with a Split Dose of Gadoterate Meglumine for Detection of Prostate Cancer. Acad Radiol 2022; 29:796-803. [PMID: 34583866 DOI: 10.1016/j.acra.2021.07.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 07/19/2021] [Accepted: 07/31/2021] [Indexed: 01/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate whether dynamic contrast enhanced (DCE) MRI with a split injection of 30% followed by 70% of a standard dose (30PSD and 70PSD) of gadoterate meglumine (DOTAREM) can improve diagnosis of prostate cancer (PCa). MATERIALS AND METHODS MRI for twenty patients was performed on a Philips Ingenia 3T scanner without an endorectal coil followed by subsequent radical prostatectomy. DCE 3D T1-FFE data were acquired with injection of 0.03 mmol/kg followed after 2 minutes by 0.07 mmol/kg of DOTAREM. Regions-of-interest on histologically verified PCa and normal tissue in different prostate zones and the iliac artery were drawn. Average signal intensity as function of time was calculated for each ROI and fitted by using the signal intensity form of the Tofts (SI-Tofts) model to extract physiological parameters (Ktrans and ve). In addition, the scaled arterial input function (AIF) obtained from 30PSD data was used to analyze 70PSD data. RESULTS The AIF obtained from 30PSD data showed both first and second passes clearly and had much higher peak magnitude than AIFs from 70PSD data. Ktrans was significantly (p < 0.05) larger in PCa than in normal tissue in peripheral zone (PZ) and central zone (CZ) for both 70PSD and 70PSD data analyzed with a scaled AIF. Ktrans in cancer overlapped with that of normal tissue in the transition zone (TZ). There was no statistical difference in ve between cancer and normal tissue. Receiver operating characteristic analysis showed that use of the AIF from 30PSD data to analyze 70PSD data increased the diagnostic efficacy of Ktrans in the PZ and CZ. CONCLUSION The split dose protocol for injection of Dotarem increased diagnostic accuracy of quantitative analysis with the SI-Tofts model.
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Urakami A, Arimura H, Takayama Y, Kinoshita F, Ninomiya K, Imada K, Watanabe S, Nishie A, Oda Y, Ishigami K. Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms. Prostate 2022; 82:330-344. [PMID: 35014713 DOI: 10.1002/pros.24278] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 11/09/2021] [Accepted: 11/23/2021] [Indexed: 01/04/2023]
Abstract
PURPOSE This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images. METHODS A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis. RESULTS The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively. CONCLUSION This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.
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Affiliation(s)
- Akimasa Urakami
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidetaka Arimura
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yukihisa Takayama
- Department of Radiology, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Fumio Kinoshita
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenta Ninomiya
- Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kenjiro Imada
- Department of Urology, Prostate, Kidney, Adrenal Surgery, Kyushu University Hospital, Fukuoka, Japan
| | - Sumiko Watanabe
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akihiro Nishie
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoshinao Oda
- Department of Anatomic Pathology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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Roytman M, Kim S, Glynn S, Thomas C, Lin E, Feltus W, Magge RS, Liechty B, Schwartz TH, Ramakrishna R, Karakatsanis NA, Pannullo SC, Osborne JR, Knisely JPS, Ivanidze J. PET/MR Imaging of Somatostatin Receptor Expression and Tumor Vascularity in Meningioma: Implications for Pathophysiology and Tumor Outcomes. Front Oncol 2022; 11:820287. [PMID: 35155210 PMCID: PMC8832502 DOI: 10.3389/fonc.2021.820287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Meningiomas, the most common primary intracranial tumor, are vascular neoplasms that express somatostatin receptor-2 (SSTR2). The purpose of this investigation was to evaluate if a relationship exists between tumor vascularity and SSTR2 expression, which may play a role in meningioma prognostication and clinical management. Materials and Methods Gallium-68-DOTATATE PET/MRI with dynamic contrast-enhanced (DCE) perfusion was prospectively performed. Clinical and demographic patient characteristics were recorded. Tumor volumes were segmented and superimposed onto parametric DCE maps including flux rate constant (Kep), transfer constant (Ktrans), extravascular volume fraction (Ve), and plasma volume fraction (Vp). Meningioma PET standardized uptake value (SUV) and SUV ratio to superior sagittal sinus (SUVRSSS) were recorded. Pearson correlation analyses were performed. In a random subset, analysis was repeated by a second investigator, and intraclass correlation coefficients (ICCs) were determined. Results Thirty-six patients with 60 meningiomas (20 WHO-1, 27 WHO-2, and 13 WHO-3) were included. Mean Kep demonstrated a strong significant positive correlation with SUV (r = 0.84, p < 0.0001) and SUVRSSS (r = 0.81, p < 0.0001). When stratifying by WHO grade, this correlation persisted in WHO-2 (r = 0.91, p < 0.0001) and WHO-3 (r = 0.92, p = 0.0029) but not WHO-1 (r = 0.26, p = 0.4, SUVRSSS). ICC was excellent (0.97–0.99). Conclusion DOTATATE PET/MRI demonstrated a strong significant correlation between tumor vascularity and SSTR2 expression in WHO-2 and WHO-3, but not WHO-1 meningiomas, suggesting biological differences in the relationship between tumor vascularity and SSTR2 expression in higher-grade meningiomas, the predictive value of which will be tested in future work.
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Affiliation(s)
- Michelle Roytman
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Sean Kim
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Shannon Glynn
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Charlene Thomas
- Weill Cornell Medical College, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Eaton Lin
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Whitney Feltus
- Departments of Radiology, New York-Presbyterian Hospital/Columbia University Medical Center, New York, NY, United States
| | - Rajiv S. Magge
- Department of Neurology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Benjamin Liechty
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Theodore H. Schwartz
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Rohan Ramakrishna
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Nicolas A. Karakatsanis
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Susan C. Pannullo
- Department of Neurological Surgery, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Joseph R. Osborne
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jonathan P. S. Knisely
- Department of Radiation Oncology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
| | - Jana Ivanidze
- Departments of Radiology, Weill Cornell Medicine/NewYork-Presbyterian Hospital, New York, NY, United States
- *Correspondence: Jana Ivanidze,
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Chinnappan S, Chandra P, Kumar JS, Chandran G, Nath S. SUVmax/ADC Ratio as a Molecular Imaging Biomarker for Diagnosis of Biopsy-Naïve Primary Prostate Cancer. Indian J Nucl Med 2021; 36:377-384. [PMID: 35125755 PMCID: PMC8771060 DOI: 10.4103/ijnm.ijnm_62_21] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/09/2021] [Accepted: 08/06/2021] [Indexed: 01/13/2023] Open
Abstract
Background: Gallium-68-prostate-specific membrane antigen (68Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has recently been shown to be very high accuracy in biopsy-naïve prostate cancer (PCa) detection and can potentially improve the low specificity noted with diffusion-weighted magnetic resonance imaging (DW-MRI), especially in instances of prostate inflammation. We aimed to compare the diagnostic accuracy of DW-MRI and PSMA PET/CT using apparent diffusion coefficient (ADC) and maximum standardized uptake (SUVmax) values in the diagnosis of PCa. Patients and Methods: A retrospective study comparing and analyzing the diagnostic accuracy of prebiopsy DW-MRI and 68Ga-PSMA PET/CTs done in patients with suspected PCa (raised prostate specific antigen [PSA] and/or positive digital rectal examination) from January 2019 to December 2020. The standard of reference was transrectal ultrasound-guided biopsies. Results: Sixty-seven patients were included in the study, mean age: 70 years (range 49–84), mean PSA: 23.2 ng/ml (range 2.97–45.6). Biopsy was positive for PCa in 56% (n = 38) and negative in 43% (n = 29). Of the benign results, benign hyperplasia was noted in 75% (n = 22) and prostatitis in 25% (n = 7). Of the PCa, 55% (n = 21) of were high International Society of Urological Pathology (ISUP) grade (4–5) and 45% (n = 17) low/intermediate ISUP grade (1–3). Overall the sensitivity/specificity/Accuracy for prediction of PCa of MRI using prostate imaging and reporting data system version 2 criteria and PSMA PET/CT using PCa molecular imaging standardized evaluation criteria was 92.1%/65.5%/80.5% and 76.3%/96.5%/85.1% respectively. Mean apparent diffusion co-efficient (mean ADC) value of benign lesions and PCa was 1.135 × 10-3 mm2/s and 0.723 × 10-3 mm2/s, respectively (P = 0.00001). Mean SUVmax and ADC of benign and PCa lesions was 4.01 and 16.4 (P = 0.000246). Mean SUVmax/ADC ratio of benign and malignant lesions was 3.8 × 103 versus 25.21 × 103 (P < 0.000026). Inverse correlation was noted between ADC and SUVmax values (R = −0.609), inverse correlation noted between ADC and Gleason's score (R = −0.198), and positive correlation of SUVmax and SUVmax/ADC with Gleason's score (R = 0.438 and R = 0.448). Receiver operating characteristic curve analysis revealed a SUVmax cutoff 6.03 (sensitivity/specificity - 76%/90%, area under the curve (AUC) - 0.935, Youden index (YI) - 0.66), ADC cutoff of 0.817 × 10−3 mm2/s (sensitivity/specificity – 79%/86%, AUC – 0.890, YI - 0.65), and SUVmax/ADC ratio cutoff of 7.43 × 103 (sensitivity/specificity – 87%/98%, AUC - 0.966, YI - 0.85) for PCa diagnosis. Conclusion: For diagnosis of biopsy-naïve PCas, the combination of diffusion-weighted MRI and PSMA PET/CT (i.e., SUVmax/ADC ratio) shows better diagnostic accuracy than either used alone and the combination of PET and MRI is especially useful when distinguishing cancer from prostatitis.
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Affiliation(s)
- Sheela Chinnappan
- Department of Radiodiagnosis, Sri Ramchandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Piyush Chandra
- Department of Nuclear Medicine, MIOT International Hospital, Chennai, Tamil Nadu, India
| | - John Santa Kumar
- Department of Nuclear Medicine, MIOT International Hospital, Chennai, Tamil Nadu, India
| | - Ganesan Chandran
- Department of Nuclear Medicine, MIOT International Hospital, Chennai, Tamil Nadu, India
| | - Satish Nath
- Department of Nuclear Medicine, MIOT International Hospital, Chennai, Tamil Nadu, India
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Yang L, Tan Y, Dan H, Hu L, Zhang J. Diagnostic performance of diffusion-weighted imaging combined with dynamic contrast-enhanced magnetic resonance imaging for prostate cancer: a systematic review and meta-analysis. Acta Radiol 2021; 62:1238-1247. [PMID: 32903025 DOI: 10.1177/0284185120956269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The diagnostic performance of diffusion-weighted imaging (DWI) combined with dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) for the detection of prostate cancer (PCa) has not been studied systematically to date. PURPOSE To investigate the value of DWI combined with DCE-MRI quantitative analysis in the diagnosis of PCa. MATERIAL AND METHODS A systematic search was conducted through PubMed, MEDLINE, the Cochrane Library, and EMBASE databases without any restriction to language up to 10 December 2019. Studies that used a combination of DWI and DCE-MRI for diagnosing PCa were included. RESULTS Nine studies with 778 participants were included. The combination of DWI and DCE-MRI provide accurate performance in diagnosing PCa with pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios of 0.79 (95% confidence interval [CI] = 0.76-0.81), 0.85 (95% CI = 0.83-0.86), 6.58 (95% CI = 3.93-11.00), 0.24 (95% CI = 0.17-0.34), and 36.43 (95% CI = 14.41-92.12), respectively. The pooled area under the summary receiver operating characteristic curve was 0.9268. Moreover, 1.5-T MR scanners demonstrated a slightly better performance than 3.0-T scanners. CONCLUSION Combined DCE-MRI and DWI could demonstrate a highly accurate area under the curve, sensitivity, and specificity for detecting PCa. More studies with large sample sizes are warranted to confirm these results.
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Affiliation(s)
- Lu Yang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Yuchuan Tan
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Hanli Dan
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
| | - Lin Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, No.39Shi-er-qiao Road, Chengdu, Sichuan, PR China
| | - Jiuquan Zhang
- Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing, PR China
- Key Laboratory for Biorheological Science and Technology of Ministry of Education (Chongqing University), Chongqing University Cancer Hospital, Chongqing, PR China
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Pharmacokinetic modeling of dynamic contrast-enhanced (DCE)-MRI in PI-RADS category 3 peripheral zone lesions: preliminary study evaluating DCE-MRI as an imaging biomarker for detection of clinically significant prostate cancers. Abdom Radiol (NY) 2021; 46:4370-4380. [PMID: 33818626 DOI: 10.1007/s00261-021-03035-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/25/2021] [Accepted: 03/03/2021] [Indexed: 01/21/2023]
Abstract
PURPOSE To determine if pharmacokinetic modeling of DCE-MRI can diagnose CS-PCa in PI-RADS category 3 PZ lesions with subjective negative DCE-MRI. MATERIALS AND METHODS In the present IRB approved, bi-institutional, retrospective, case-control study, we identified 73 men with 73 PZ PI-RADS version 2.1 category 3 lesions with MRI-directed-TRUS-guided targeted biopsy yielding: 12 PZ CS-PCa (ISUP Grade Group 2; N = 9, ISUP 3; N = 3), 27 ISUP 1 PCa and 34 benign lesions. An expert blinded radiologist segmented lesions on ADC and DCE images; segmentations were overlayed onto pharmacokinetic DCE-MRI maps. Mean values were compared between groups using univariate analysis. Diagnostic accuracy was assessed by ROC. RESULTS There were no differences in age, PSA, PSAD or clinical stage between groups (p = 0.265-0.645). Mean and 10th percentile ADC did not differ comparing CS-PCa to ISUP 1 PCa and benign lesions (p = 0.376 and 0.598) but was lower comparing ISUP ≥ 1 PCa to benign lesions (p < 0.001). Mean Ktrans (p = 0.003), Ve (p = 0.003) but not Kep (p = 0.387) were higher in CS-PCa compared to ISUP 1 PCa and benign lesions. There were no differences in DCE-MRI metrics comparing ISUP ≥ 1 PCa and benign lesions (p > 0.05). AUC for diagnosis of CS-PCa using Ktrans and Ve were: 0.69 (95% CI 0.52-0.87) and 0.69 (0.49-0.88). CONCLUSION Pharmacokinetic modeling of DCE-MRI parameters in PI-RADS category 3 lesions with subjectively negative DCE-MRI show significant differences comparing CS-PCa to ISUP 1 PCa and benign lesions, in this study outperforming ADC. Studies are required to further evaluate these parameters to determine which patients should undergo targeted biopsy for PI-RADS 3 lesions.
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Nair KS, Srivastava C, Brown RV, Koli S, Choquet H, Kang HS, Kuo YM, Grimm SA, Sutherland C, Badea A, Johnson GA, Zhao Y, Yin J, Okamoto K, Clark G, Borrás T, Zode G, Kizhatil K, Chakrabarti S, John SWM, Jorgenson E, Jetten AM. GLIS1 regulates trabecular meshwork function and intraocular pressure and is associated with glaucoma in humans. Nat Commun 2021; 12:4877. [PMID: 34385434 PMCID: PMC8361148 DOI: 10.1038/s41467-021-25181-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/21/2021] [Indexed: 01/01/2023] Open
Abstract
Chronically elevated intraocular pressure (IOP) is the major risk factor of primary open-angle glaucoma, a leading cause of blindness. Dysfunction of the trabecular meshwork (TM), which controls the outflow of aqueous humor (AqH) from the anterior chamber, is the major cause of elevated IOP. Here, we demonstrate that mice deficient in the Krüppel-like zinc finger transcriptional factor GLI-similar-1 (GLIS1) develop chronically elevated IOP. Magnetic resonance imaging and histopathological analysis reveal that deficiency in GLIS1 expression induces progressive degeneration of the TM, leading to inefficient AqH drainage from the anterior chamber and elevated IOP. Transcriptome and cistrome analyses identified several glaucoma- and extracellular matrix-associated genes as direct transcriptional targets of GLIS1. We also identified a significant association between GLIS1 variant rs941125 and glaucoma in humans (P = 4.73 × 10-6), further supporting a role for GLIS1 into glaucoma etiology. Our study identifies GLIS1 as a critical regulator of TM function and maintenance, AqH dynamics, and IOP.
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Affiliation(s)
- K Saidas Nair
- Department of Ophthalmology and Department of Anatomy, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Chitrangda Srivastava
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Robert V Brown
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Swanand Koli
- Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Hélène Choquet
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - Hong Soon Kang
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Yien-Ming Kuo
- Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Sara A Grimm
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Caleb Sutherland
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Alexandra Badea
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, USA
| | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, NC, USA
| | - Yin Zhao
- Department of Ophthalmology, School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jie Yin
- Kaiser Permanente Northern California, Division of Research, Oakland, CA, USA
| | - Kyoko Okamoto
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | | | - Terete Borrás
- Department of Ophthalmology, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Gulab Zode
- Department of Pharmacology and Neuroscience, North Texas Eye Research Institute, University of North Texas Health Science Center, Fort Worth, TX, USA
| | | | | | - Simon W M John
- The Jackson Laboratory, Bar Harbor, ME, USA
- Howard Hughes Medical Institute, Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Ophthalmology, Columbia University, New York, NY, USA
| | | | - Anton M Jetten
- Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA.
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Gholizadeh N, Greer PB, Simpson J, Goodwin J, Fu C, Lau P, Siddique S, Heerschap A, Ramadan S. Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection. J Biomed Sci 2021; 28:54. [PMID: 34281540 PMCID: PMC8290561 DOI: 10.1186/s12929-021-00750-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/10/2021] [Indexed: 12/24/2022] Open
Abstract
Background Current multiparametric MRI (mp-MRI) in routine clinical practice has poor-to-moderate diagnostic performance for transition zone prostate cancer. The aim of this study was to evaluate the potential diagnostic performance of novel 1H magnetic resonance spectroscopic imaging (MRSI) using a semi-localized adiabatic selective refocusing (sLASER) sequence with gradient offset independent adiabaticity (GOIA) pulses in addition to the routine mp-MRI, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) and quantitative dynamic contrast enhancement (DCE) for transition zone prostate cancer detection, localization and grading. Methods Forty-one transition zone prostate cancer patients underwent mp-MRI with an external phased-array coil. Normal and cancer regions were delineated by two radiologists and divided into low-risk, intermediate-risk, and high-risk categories based on TRUS guided biopsy results. Support vector machine models were built using different clinically applicable combinations of T2WI, DWI, DCE, and MRSI. The diagnostic performance of each model in cancer detection was evaluated using the area under curve (AUC) of the receiver operating characteristic diagram. Then accuracy, sensitivity and specificity of each model were calculated. Furthermore, the correlation of mp-MRI parameters with low-risk, intermediate-risk and high-risk cancers were calculated using the Spearman correlation coefficient. Results The addition of MRSI to T2WI + DWI and T2WI + DWI + DCE improved the accuracy, sensitivity and specificity for cancer detection. The best performance was achieved with T2WI + DWI + MRSI where the addition of MRSI improved the AUC, accuracy, sensitivity and specificity from 0.86 to 0.99, 0.83 to 0.96, 0.80 to 0.95, and 0.85 to 0.97 respectively. The (choline + spermine + creatine)/citrate ratio of MRSI showed the highest correlation with cancer risk groups (r = 0.64, p < 0.01). Conclusion The inclusion of GOIA-sLASER MRSI into conventional mp-MRI significantly improves the diagnostic accuracy of the detection and aggressiveness assessment of transition zone prostate cancer.
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Affiliation(s)
- Neda Gholizadeh
- School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia
| | - Peter B Greer
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia.,Calvary Mater Newcastle, Radiation Oncology Department, Newcastle, NSW, Australia
| | - John Simpson
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia.,Calvary Mater Newcastle, Radiation Oncology Department, Newcastle, NSW, Australia
| | - Jonathan Goodwin
- School of Mathematical and Physical Sciences, University of Newcastle, Newcastle, NSW, Australia.,Calvary Mater Newcastle, Radiation Oncology Department, Newcastle, NSW, Australia
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Peter Lau
- Radiology Department, Calvary Mater Newcastle, Newcastle, NSW, Australia.,Hunter Medical Research Institute (HMRI) Imaging Centre, New Lambton Heights, NSW, Australia
| | - Saabir Siddique
- Radiology Department, Calvary Mater Newcastle, Newcastle, NSW, Australia.,Hunter Medical Research Institute (HMRI) Imaging Centre, New Lambton Heights, NSW, Australia
| | - Arend Heerschap
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Saadallah Ramadan
- School of Health Sciences, Faculty of Health and Medicine, University of Newcastle, Newcastle, NSW, Australia. .,Hunter Medical Research Institute (HMRI) Imaging Centre, New Lambton Heights, NSW, Australia.
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Detection of Immunotherapeutic Response in a Transgenic Mouse Model of Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI Radiomics: A Preliminary Investigation. Acad Radiol 2021; 28:e147-e154. [PMID: 32499156 DOI: 10.1016/j.acra.2020.04.026] [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: 03/29/2020] [Revised: 04/14/2020] [Accepted: 04/16/2020] [Indexed: 12/24/2022]
Abstract
RATIONALE AND OBJECTIVES To develop classification and regression models interpreting tumor characteristics obtained from structural (T1w and T2w) magnetic resonance imaging (MRI) data for early detection of dendritic cell (DC) vaccine treatment effects and prediction of long-term outcomes for LSL-KrasG12D; LSL-Trp53R172H; Pdx-1-Cre (KPC) transgenic mice model of pancreatic ductal adenocarcinoma. MATERIALS AND METHODS Eight mice were treated with DC vaccine for 3 weeks while eight KPC mice were used as untreated control subjects. The reproducibility of the computed 264 features was evaluated using the intraclass correlation coefficient. Key variables were determined using a three-step feature selection approach. Support vector machines classifiers were generated to differentiate treatment-related changes on tumor tissue following first- and third weeks of the DC vaccine therapy. The multivariable regression models were generated to predict overall survival (OS) and histological tumor markers of KPC mice using quantitative features. RESULTS The quantitative features computed from T1w MRI data have better reproducibility than T2w MRI features. The KPC mice in treatment and control groups were differentiated with a longitudinally increasing accuracy (first- and third weeks: 87.5% and 93.75%). The linear regression model generated with five features of T1w MRI data predicted OS with a root-mean-squared error (RMSE) <6 days. The proposed multivariate regression models predicted histological tumor markers with relative error <2.5% for fibrosis percentage (RMSE: 0.414), CK19+ area (RMSE: 0.027), and Ki67+ cells (RMSE: 0.190). CONCLUSION Our results demonstrated that proposed models generated with quantitative MRI features can be used to detect early treatment-related changes in tumor tissue and predict OS of KPC mice following DC vaccination.
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Ingole SM, Mehta RU, Kazi ZN, Bhuyar RV. Multiparametric Magnetic Resonance Imaging in Evaluation of Clinically Significant Prostate Cancer. Indian J Radiol Imaging 2021; 31:65-77. [PMID: 34316113 PMCID: PMC8299509 DOI: 10.1055/s-0041-1730093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Aim
In this prospective study, we evaluate the role of multiparametric magnetic resonance imaging (mp-MRI) in the assessment of clinically significant prostate cancer at 1.5 T without endorectal coil (ERC).
Materials and Methods
Forty-five men with clinical suspicion of prostate cancer (prostate-specific antigen [PSA] level > 4 ng/mL, hard prostate on digital rectal examination, and suspicious area at transrectal ultrasound [TRUS]) were evaluated using the mp-MRI protocol over a period of 24 months. All cases were interpreted using the Prostate Imaging Reporting and Data System (PI-RADS) version 2 guidelines and correlated with histopathology.
Statistical Analysis Used
A chi-squared test was used for analysis of nominal/categorical variables and receiver operating characteristic (ROC) curve and one-way analysis of variance (ANOVA) test for continuous variables.
Results
The mean age was 67 years and the mean PSA was 38.2 ng/mL. Eighty percent had prostate cancer and 20% were benign (11% benign prostatic hyperplasia [BPH] and 9% chronic prostatitis). Eighty-six percent of all malignancies were in the peripheral zone. The PI-RADS score for T2-weighted (T2W) imaging showed good sensitivity (81%) but low specificity (67%). The PI-RADS score for diffusion weighted imaging (DWI) with sensitivity of 92% and specificity of 78% had a better accuracy overall than T2W imaging alone. The mean apparent diffusion coefficient (ADC) value (×10
–6
mm
2
/s) was 732 ± 160 in prostate cancer, 1,009 ± 161 in chronic prostatitis, 1,142 ± 82 in BPH, and 663 in a single case of granulomatous prostatitis. Low ADC values (<936) have shown good correlation (area under curve [AUC]: 0.87) with the presence of cancer foci. Inverse correlation was observed between Gleason scores and ADC values. Dynamic contrast-enhanced (DCE) imaging has shown 100% sensitivity/negative predictive value (NPV), but moderate specificity (67%) in predicting malignancy. The final PI-RADS score had 100% sensitivity and NPV with good overall positive predictive value (PPV) of 95%.
Conclusions
T2W imaging and DWI remain the mainstays in diagnosis of prostate cancer with mp-MRI. DCE-MRI can be a problem-solving tool in case of equivocal findings. Because assessment with mp-MRI can be subjective, use of the newly developed PI-RADS version 2 scoring system is helpful in accurate interpretation.
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Affiliation(s)
- Sarang M Ingole
- Department of Imaging Sciences and Pathology, Saifee Hospital, Mumbai, Maharashtra, India
| | - Rajeev U Mehta
- Department of Imaging Sciences and Pathology, Saifee Hospital, Mumbai, Maharashtra, India
| | - Zubair N Kazi
- Department of Imaging Sciences and Pathology, Saifee Hospital, Mumbai, Maharashtra, India
| | - Rutuja V Bhuyar
- Department of Imaging Sciences and Pathology, Saifee Hospital, Mumbai, Maharashtra, India
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Avery A, Sussman M, Longo J, Menezes RJ, Hamilton RJ, van der Kwast TH, Fleshner NE, Penn LZ, Ghai S. Quantitative Prostate MRI Analysis Following Fluvastatin Therapy for Localized Prostate Cancer - A Pilot Study. Can Assoc Radiol J 2021; 72:750-758. [PMID: 33563030 DOI: 10.1177/0846537120988262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
PURPOSE To assess the role of multi-parametric MRI (mpMRI) in assessment of tumor response to fluvastatin administered prior to radical prostatectomy. METHODS Men with MRI-visible, clinically significant prostate cancer and due to be treated with radical prostatectomy were prospectively enrolled. mpMRI was performed at baseline and following 6-7 week of neoadjuvant oral statin therapy (40 mg fluvastatin, twice daily), prior to prostatectomy. MRI assessment included tumor size, T2 relaxation time, ADC value, K-trans (volume transfer constant), Kep (reflux constant), and Ve (fractional volume) parameters at the 2 time points. Initial prostate needle biopsy cores, prior to starting oral statin therapy, corresponding to site of tumor on radical prostatectomy specimens were selected for analysis. The effect of fluvastatin on tumor proliferation (marker Ki67) and on tumor cell apoptosis (marker cleaved Caspase-3, CC3) were analyzed and correlated with MRI findings. RESULTS Nine men with paired MRI studies were included in the study. Binary histopathological data was available for 6 of the participants. No significant change in tumor size (P = 0.898), T2 relaxation time (P = 0.213), ADC value (P = 0.455), K-trans (P = 0.613), Kep (P = 0.547) or Ve (P = 0.883) between the time of biopsy and prostatectomy were observed. No significant change in tumor proliferation (%Ki67-positive cells, P = 0.766) was observed by immunohistochemistry analysis. However, there was a significant increase in tumor cell apoptosis (%CC3-positive cells, P = 0.047). CONCLUSION mpMRI techniques may not be sufficiently sensitive to detect the types (or magnitude) of tumor cell changes observed following 6-7 weeks of fluvastatin therapy for prostate cancer.
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Affiliation(s)
- Allan Avery
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Marshall Sussman
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Joseph Longo
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Ravi J Menezes
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Robert J Hamilton
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Division of Urology, Department of Surgical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Theodorus H van der Kwast
- Department of Pathology, Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada
| | - Neil E Fleshner
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Division of Urology, Department of Surgical Oncology, University Health Network, Toronto, Ontario, Canada
| | - Linda Z Penn
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Sangeet Ghai
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
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Shao W, Banh L, Kunder CA, Fan RE, Soerensen SJC, Wang JB, Teslovich NC, Madhuripan N, Jawahar A, Ghanouni P, Brooks JD, Sonn GA, Rusu M. ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate. Med Image Anal 2021; 68:101919. [PMID: 33385701 PMCID: PMC7856244 DOI: 10.1016/j.media.2020.101919] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 11/18/2020] [Accepted: 11/23/2020] [Indexed: 12/21/2022]
Abstract
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI suffers from high inter-observer variability across radiologists, thereby contributing to missed clinically significant cancers, overdiagnosed low-risk cancers, and frequent false positives. Interpretation of MRI could be greatly improved by providing radiologists with an answer key that clearly shows cancer locations on MRI. Registration of histopathology images from patients who had radical prostatectomy to pre-operative MRI allows such mapping of ground truth cancer labels onto MRI. However, traditional MRI-histopathology registration approaches are computationally expensive and require careful choices of the cost function and registration hyperparameters. This paper presents ProsRegNet, a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of image preprocessing, estimation of affine and deformable transformations by deep neural networks, and mapping cancer labels from histopathology images onto MRI using estimated transformations. We trained our neural network using MR and histopathology images of 99 patients from our internal cohort (Cohort 1) and evaluated its performance using 53 patients from three different cohorts (an additional 12 from Cohort 1 and 41 from two public cohorts). Results show that our deep learning pipeline has achieved more accurate registration results and is at least 20 times faster than a state-of-the-art registration algorithm. This important advance will provide radiologists with highly accurate prostate MRI answer keys, thereby facilitating improvements in the detection of prostate cancer on MRI. Our code is freely available at https://github.com/pimed//ProsRegNet.
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Affiliation(s)
- Wei Shao
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
| | - Linda Banh
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Richard E Fan
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | | | - Jeffrey B Wang
- School of Medicine, Stanford University, Stanford, CA 94305, USA
| | | | - Nikhil Madhuripan
- Department of Radiology, University of Colorado, Aurora, CO 80045, USA
| | | | - Pejman Ghanouni
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - James D Brooks
- Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Geoffrey A Sonn
- Department of Radiology, Stanford University, Stanford, CA 94305, USA; Department of Urology, Stanford University, Stanford, CA 94305, USA
| | - Mirabela Rusu
- Department of Radiology, Stanford University, Stanford, CA 94305, USA.
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41
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Eusebi L, Carpagnano FA, Sortino G, Bartelli F, Guglielmi G. Prostate Multiparametric MRI: Common Pitfalls in Primary Diagnosis and How to Avoid Them. CURRENT RADIOLOGY REPORTS 2021. [DOI: 10.1007/s40134-021-00378-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Abstract
Purpose of Review
To provide the radiologist with basic knowledge about normal and abnormal findings in the prostatic mp-MRI, taking a look at the possible diagnostic pitfalls commonly seen in daily clinical practice, allowing him to recognize and consequently avoid them.
Recent Findings
Prostate mp-MRI has now become commonly used in most diagnostic imaging centers, as a precise, accurate and above all non-invasive tool, useful in the diagnosis, staging and follow-up of prostate diseases, first of all prostatic carcinoma. For this reason, it is important to take into account the existence of numerous possible anatomic and pathologic processes which can mimick or masquerade as prostate cancer.
Summary
Through the combination of anatomical (T2WI) and functional sequences (DWI/ADC and DCE), the mp-MRI of the prostate provides all the information necessary for a correct classification of patients with prostate disease, cancer in particular. It is not uncommon, however, for the radiologist to make errors in the interpretation of imaging due to conditions, pathological or otherwise, that mimic prostate cancer and that, consequently, affect the diagnostic/therapeutic process of patients. The strategy, and what this pictorial review aims at, is to learn to recognize the potential pitfalls of the prostatic mp-MRI and avoid them.
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42
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Wu RC, Lebastchi AH, Hadaschik BA, Emberton M, Moore C, Laguna P, Fütterer JJ, George AK. Role of MRI for the detection of prostate cancer. World J Urol 2021; 39:637-649. [PMID: 33394091 DOI: 10.1007/s00345-020-03530-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 11/13/2020] [Indexed: 01/24/2023] Open
Abstract
The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.
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Affiliation(s)
- Richard C Wu
- Department of Urology, E-Da Hospital, Kaohsiung, Taiwan
- College of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Amir H Lebastchi
- Department of Urology, University of Southern California, Los Angeles, CA, USA
| | - Boris A Hadaschik
- University Hospital Heidelberg and German Cancer Research Center, Heidelberg, Germany
| | - Mark Emberton
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Caroline Moore
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Pilar Laguna
- Department of Urology, Medipol University Research Hospital, Istanbul, Turkey
| | - Jurgen J Fütterer
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Arvin K George
- Department of Urology, University of Michigan, Ann Arbor, MI, USA.
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43
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Zhou X, Fan X, Mustafi D, Pineda F, Markiewicz E, Zamora M, Sheth D, Olopade OI, Oto A, Karczmar GS. Comparison of DCE-MRI of murine model cancers with a low dose and high dose of contrast agent. Phys Med 2021; 81:31-39. [PMID: 33373779 DOI: 10.1016/j.ejmp.2020.11.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 10/27/2020] [Accepted: 11/19/2020] [Indexed: 02/08/2023] Open
Abstract
There are increasing concerns regarding intracellular accumulation of gadolinium (Gd) after multiple dynamic contrast enhanced (DCE) MRI scans. We investigated whether a low dose (LD) of Gd-based contrast agent is as effective as a high dose (HD) for quantitative analysis of DCE-MRI data, and evaluated the use of a split dose protocol to obtain new diagnostic parameters. Female C3H mice (n = 6) were injected with mammary carcinoma cells in the hind leg. MRI experiments were performed on 9.4 T scanner. DCE-MRI data were acquired with 1.5 s temporal resolution before and after a LD (0.04 mmol/kg), then again after 30 min followed by a HD (0.2 mmol/kg) bolus injection of Omniscan. The standard Tofts model was used to extract physiological parameters (Ktrans and ve) with the arterial input function derived from muscle reference tissue. In addition, an empirical mathematical model was used to characterize maximum contrast agent uptake (A), contrast agent uptake rate (α) and washout rate (β and γ). There were moderate to strong correlations (r = 0.69-0.97, p < 0001) for parameters Ktrans, ve, A, α and β from LD versus HD data. On average, tumor parameters obtained from LD data were significantly larger (p < 0.05) than those from HD data. The parameter ratios, Ktrans, ve, A and α calculated from the LD data divided by the HD data, were all significantly larger than 1.0 (p < 0.003) for tumor. T2* changes following contrast agent injection affected parameters calculated from HD data, but this was not the case for LD data. The results suggest that quantitative analysis of LD data may be at least as effective for cancer characterization as quantitative analysis of HD data. In addition, the combination of parameters from two different doses may provide useful diagnostic information.
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Affiliation(s)
- Xueyan Zhou
- School of Technology, Harbin University, Harbin, China; Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Xiaobing Fan
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Devkumar Mustafi
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Federico Pineda
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Erica Markiewicz
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Marta Zamora
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Deepa Sheth
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | | | - Aytekin Oto
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States
| | - Gregory S Karczmar
- Department of Radiology, The University of Chicago, Chicago, IL 60637, United States.
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Sellers J, Wagstaff RG, Helo N, de Riese WTW. Quantitative measurements of prostatic zones by MRI and their dependence on prostate size: possible clinical implications in prostate cancer. Ther Adv Urol 2021; 13:17562872211000852. [PMID: 33868460 PMCID: PMC8020739 DOI: 10.1177/17562872211000852] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 02/12/2021] [Indexed: 12/15/2022] Open
Abstract
AIM Many studies support an inverse relationship between benign prostate hypertrophy (BPH) size and incidence of prostate cancer (PCa), but the causal link between these conditions is poorly understood. Recent studies suggest that a growing transition zone (TZ) in the prostate may induce pressure on the outer peripheral zone (PZ), leading to atrophy of the glandular tissue where PCa often originates, providing a possible explanation for this interaction. To further investigate this phenomenon, our pilot study uses magnetic resonance imaging (MRI) to examine quantitative zonal changes in a consecutive cohort of prostates. METHODS MRI scans of male patients [n = 204, 61.57 ± 13.90 years, average body mass index (BMI) 29.05 kg/m2] with various prostate sizes were analyzed statistically to identify possible associations between prostate parameters, such as total prostate volume (TPV) and peripheral zone thickness (PZT). RESULTS TPV and PZT demonstrated a weak, inverse correlation (r = -0.21, p = 0.002). However, when examining the plotted data, the relationship between TPV and PZT was significantly different when the cohort was divided into two groups; lower TPV: ⩽87.5 ml (n = 188, TPV x- = 36.01 ± 18.18 ml), and higher TPV: >87.5 ml (n = 17, TPV x- = 125.69 ± 41.13 ml). Average PZT differed significantly between these groups (z = -3.5554, p = 0.0004). CONCLUSIONS PZT was significantly different for patients with lower versus higher TPVs, suggesting that, above a certain point of BPH growth, the PZ is unable to withstand pressure from an expanding TZ, supporting the notion that growing BPH causes compression of the PZ glandular tissue, and, therefore, BPH may be protective against PCa.
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Affiliation(s)
- Jake Sellers
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Rachel G. Wagstaff
- Department of Urology, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Naseem Helo
- Department of Radiology, University Medical Center, Lubbock, TX, USA
| | - Werner T. W. de Riese
- Department of Urology, Texas Tech University Health Sciences Center – School of Medicine, 3601 4th Street, Lubbock, TX 79430-7260, USA
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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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46
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Is quantitative DCE-MRI useful in differentiation of indolent and significant prostate cancers? JOURNAL OF SURGERY AND MEDICINE 2020. [DOI: 10.28982/josam.840971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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47
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Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI? AJR Am J Roentgenol 2020; 215:1104-1112. [PMID: 32901562 DOI: 10.2214/ajr.20.22843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (Ktrans), reverse volume transfer constant (kep), plasma volume fraction (Vp), extravascular extracellular space volume fraction (Ve), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (Ktrans, kep, Ve, and Vp), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that Ve values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, kep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.
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Jiang M, Shen Q, Li Y, Yang X, Zhang J, Wang Y, Xia L. Improved robust tensor principal component analysis for accelerating dynamic MR imaging reconstruction. Med Biol Eng Comput 2020; 58:1483-1498. [DOI: 10.1007/s11517-020-02161-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 03/12/2020] [Indexed: 11/30/2022]
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Mussi TC, Baroni RH, Zagoria RJ, Westphalen AC. Prostate magnetic resonance imaging technique. Abdom Radiol (NY) 2020; 45:2109-2119. [PMID: 31701190 DOI: 10.1007/s00261-019-02308-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Multiparametric magnetic resonance (MR) imaging of the prostate is an excellent tool to detect clinically significant prostate cancer, and it has widely been incorporated into clinical practice due to its excellent tissue contrast and image resolution. The aims of this article are to describe the prostate MR imaging technique for detection of clinically significant prostate cancer according to PI-RADS v2.1, as well as alternative sequences and basic aspects of patient preparation and MR imaging artifact avoidance.
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Wildeboer RR, van Sloun RJG, Wijkstra H, Mischi M. Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105316. [PMID: 31951873 DOI: 10.1016/j.cmpb.2020.105316] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 12/09/2019] [Accepted: 01/04/2020] [Indexed: 05/16/2023]
Abstract
Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.
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Affiliation(s)
- Rogier R Wildeboer
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Ruud J G van Sloun
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands.
| | - Hessel Wijkstra
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands; Department of Urology, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, the Netherlands
| | - Massimo Mischi
- Lab of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, De Zaale, 5600 MB, Eindhoven, the Netherlands
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