1
|
de Oliveira Correia ET, Purysko AS, Paranhos BM, Shoag JE, Padhani AR, Bittencourt LK. PI-RADS Upgrading Rules: Impact on Prostate Cancer Detection and Biopsy Avoidance of MRI-Directed Diagnostic Pathways. AJR Am J Roentgenol 2024; 222:e2330611. [PMID: 38353450 DOI: 10.2214/ajr.23.30611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
BACKGROUND. PI-RADS incorporates rules by which ancillary sequence findings upgrade a dominant score to a higher final category. Evidence on the upgrading rules' impact on diagnostic pathways remains scarce. OBJECTIVE. The purpose of this article was to evaluate the clinical net benefit of the PI-RADS upgrading rules in MRI-directed diagnostic pathways. METHODS. This study was a retrospective analysis of a prospectively maintained clinical registry. The study included patients without known prostate cancer who underwent prostate MRI followed by prostate biopsy from January 2016 to May 2020. Clinically significant prostate cancer (csPCa) was defined as International Society of Urological Pathology (ISUP) grade group 2 and higher. csPCa detection was compared between dominant (i.e., no upgrade rule applied) and upgraded lesions. Decision-curve analysis was used to compare the net benefit, considering the trade-off of csPCa detection and biopsy avoidance, of MRI-directed pathways in scenarios considering and disregarding PI-RADS upgrading rules. These included a biopsy-all pathway, MRI-focused pathway (no biopsy for PI-RADS ≤ 2), and risk-based pathway (use of PSA density ≥ 0.15 ng/mL2 to select patients with PI-RADS ≤ 3 for biopsy). RESULTS. The sample comprised 716 patients (mean age, 64.9 years; 93 with a PI-RADS ≤ 2 examination, 623 with total of 780 PI-RADS ≥ 3 lesions). Frequencies of csPCa were not significantly different between dominant and upgraded PI-RADS 3 transition zone lesions (20% vs 19%, respectively), dominant and upgraded PI-RADS 4 transition zone lesions (33% vs 26%), and dominant and upgraded PI-RADS 4 peripheral zone lesions (58% vs 45%) (p > .05). In the biopsy-all, per-guideline MRI-focused, MRI-focused disregarding upgrading rules, per-guideline risk-based, and risk-based disregarding upgrading rules pathways, csPCa frequency was 53%, 52%, 51%, 52%, and 48% and biopsy avoidance was 0%, 13%, 16%, 19%, and 25%, respectively. Disregarding upgrading rules yielded 5.5 and 1.9 biopsies avoided per missed csPCa for MRI-focused and risk-based pathways, respectively. At probability thresholds for biopsy selection of 7.5-30.0%, net benefit was highest for the per-guideline risk-based pathway. CONCLUSION. Disregarding PI-RADS upgrading rules reduced net clinical bene fit of the risk-based MRI-directed diagnostic pathway when considering trade-offs between csPCa detection and biopsy avoidance. CLINICAL IMPACT. This study supports the application of PI-RADS upgrading rules to optimize biopsy selection, particularly in risk-based pathways.
Collapse
Affiliation(s)
| | - Andrei S Purysko
- Department of Radiology, Abdominal Imaging Section, Cleveland Clinic, Cleveland, OH
| | - Bruno Merz Paranhos
- Department of Radiology, Diagnosticos da America S.A, Rio de Janeiro, Brazil
| | - Jonathan E Shoag
- Case Western Reserve University, Cleveland, OH
- Case Comprehensive Cancer Center, Cleveland, OH
- Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, OH
- Department of Urology, Weill Cornell Medicine, New York, NY
| | - Anwar R Padhani
- Paul Strickland Scanner Centre, Mount Vernon Hospital, Middlesex, United Kingdom
| | - Leonardo K Bittencourt
- Department of Radiology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, Ohio 44106
- Case Western Reserve University, Cleveland, OH
| |
Collapse
|
2
|
Beetz NL, Dräger F, Hamm CA, Shnayien S, Rudolph MM, Froböse K, Elezkurtaj S, Haas M, Asbach P, Hamm B, Mahjoub S, Konietschke F, Wechsung M, Balzer F, Cash H, Hofbauer S, Penzkofer T. MRI-targeted biopsy cores from prostate index lesions: assessment and prediction of the number needed. Prostate Cancer Prostatic Dis 2023; 26:543-551. [PMID: 36209237 PMCID: PMC10449625 DOI: 10.1038/s41391-022-00599-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/08/2022] [Accepted: 09/23/2022] [Indexed: 11/09/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model. METHODS We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates. RESULTS The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model. CONCLUSION The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates.
Collapse
Affiliation(s)
- Nick Lasse Beetz
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany.
| | - Franziska Dräger
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Charlie Alexander Hamm
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Seyd Shnayien
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Madhuri Monique Rudolph
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Konrad Froböse
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sefer Elezkurtaj
- Department of Pathology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Matthias Haas
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Patrick Asbach
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Samy Mahjoub
- Department of Urology, Medizinische Hochschule Hannover, Hannover, Germany
| | - Frank Konietschke
- Institute of Biometry and Clinical Epidemiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Maximilian Wechsung
- Institute of Biometry and Clinical Epidemiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Hannes Cash
- Department of Urology, University Hospital Magdeburg, Magdeburg, Sachsen-Anhalt, Germany
| | - Sebastian Hofbauer
- Department of Urology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charité University Hospital Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, Berlin, Germany
| |
Collapse
|
3
|
Zhu M, Liang Z, Feng T, Mai Z, Jin S, Wu L, Zhou H, Chen Y, Yan W. Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review. Diagnostics (Basel) 2023; 13:2283. [PMID: 37443677 DOI: 10.3390/diagnostics13132283] [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: 05/18/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.
Collapse
Affiliation(s)
- Ming Zhu
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhen Liang
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Tianrui Feng
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Zhipeng Mai
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Shijie Jin
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Liyi Wu
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Huashan Zhou
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yuliang Chen
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Weigang Yan
- Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| |
Collapse
|
4
|
Yilmaz EC, Shih JH, Belue MJ, Harmon SA, Phelps TE, Garcia C, Hazen LA, Toubaji A, Merino MJ, Gurram S, Choyke PL, Wood BJ, Pinto PA, Turkbey B. Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers. Radiology 2023; 307:e221309. [PMID: 37129493 PMCID: PMC10323290 DOI: 10.1148/radiol.221309] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 01/21/2023] [Accepted: 02/10/2023] [Indexed: 05/03/2023]
Abstract
Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.
Collapse
Affiliation(s)
- Enis C. Yilmaz
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Joanna H. Shih
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Mason J. Belue
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Stephanie A. Harmon
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Tim E. Phelps
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Charisse Garcia
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Lindsey A. Hazen
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Antoun Toubaji
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Maria J. Merino
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Sandeep Gurram
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Peter L. Choyke
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Bradford J. Wood
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Peter A. Pinto
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| | - Baris Turkbey
- From the Molecular Imaging Branch (E.C.Y., M.J.B., S.A.H., T.E.P.,
P.L.C., B.T.), Biometric Research Program, Division of Cancer Treatment and
Diagnosis (J.H.S.), Center for Interventional Oncology (C.G., L.A.H., B.J.W.),
Department of Radiology, Clinical Center (C.G., L.A.H., B.J.W.), Laboratory of
Pathology (A.T., M.J.M.), and Urologic Oncology Branch (S.G., P.A.P.), National
Cancer Institute, National Institutes of Health, 10 Center Dr, MSC 1182,
Building 10, Room B3B85, Bethesda, MD 20892
| |
Collapse
|
5
|
Di Franco F, Souchon R, Crouzet S, Colombel M, Ruffion A, Klich A, Almeras M, Milot L, Rabilloud M, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study). Insights Imaging 2023; 14:49. [PMID: 36939970 PMCID: PMC10027981 DOI: 10.1186/s13244-023-01391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023] Open
Abstract
OBJECTIVE To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience. METHODS Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement. RESULTS At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results. CONCLUSIONS Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.
Collapse
Affiliation(s)
- Florian Di Franco
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
| | | | - Sébastien Crouzet
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Marc Colombel
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Alain Ruffion
- Université de Lyon, Université Lyon 1, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
- Equipe 2-Centre d'Innovation en Cancérologie de Lyon, 3738, Lyon, EA, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Amna Klich
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Mathilde Almeras
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Laurent Milot
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Muriel Rabilloud
- Université de Lyon, Université Lyon 1, Lyon, France
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Olivier Rouvière
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France.
- INSERM, LabTau, U1032, Lyon, France.
- Université de Lyon, Université Lyon 1, Lyon, France.
- Faculté de Médecine Lyon Est, Lyon, France.
| | | |
Collapse
|
6
|
Kim CK. [Prostate Imaging Reporting and Data System (PI-RADS) v 2.1: Overview and Critical Points]. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:75-91. [PMID: 36818694 PMCID: PMC9935951 DOI: 10.3348/jksr.2022.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/15/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023]
Abstract
The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.
Collapse
Affiliation(s)
- Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| |
Collapse
|
7
|
Guo Z, Qin X, Mu R, Lv J, Meng Z, Zheng W, Zhuang Z, Zhu X. Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate. J Magn Reson Imaging 2022; 56:1311-1319. [PMID: 35429190 DOI: 10.1002/jmri.28204] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 04/04/2022] [Accepted: 04/04/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI. PURPOSE To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa. STUDY TYPE Prospective. POPULATION A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92). FIELD STRENGTH/SEQUENCE A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE. ASSESSMENT Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively). STATISTICAL TESTS Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05. RESULTS APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10-3 mm2 /sec vs. stromal BPH 0.91 ± 0.14 × 10-3 mm2 /sec vs. glandular BPH 1.08 ± 0.18 × 10-3 mm2 /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH. DATA CONCLUSION APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Zixuan Guo
- Department of Medical Imaging, Guilin Medical University, Guilin, China
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Xiaoyan Qin
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Ronghua Mu
- Department of Medical Imaging, Guilin Medical University, Guilin, China
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Jian Lv
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Zhuoni Meng
- Department of Medical Imaging, Guilin Medical University, Guilin, China
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Wei Zheng
- Department of Medical Imaging, Guilin Medical University, Guilin, China
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Zeyu Zhuang
- Department of Medical Imaging, Guilin Medical University, Guilin, China
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| | - Xiqi Zhu
- Department of Medical Imaging, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
| |
Collapse
|
8
|
Zhu L, Gao G, Zhu Y, Han C, Liu X, Li D, Liu W, Wang X, Zhang J, Zhang X, Wang X. Fully automated detection and localization of clinically significant prostate cancer on MR images using a cascaded convolutional neural network. Front Oncol 2022; 12:958065. [PMID: 36249048 PMCID: PMC9558117 DOI: 10.3389/fonc.2022.958065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To develop a cascaded deep learning model trained with apparent diffusion coefficient (ADC) and T2-weighted imaging (T2WI) for fully automated detection and localization of clinically significant prostate cancer (csPCa). Methods This retrospective study included 347 consecutive patients (235 csPCa, 112 non-csPCa) with high-quality prostate MRI data, which were randomly selected for training, validation, and testing. The ground truth was obtained using manual csPCa lesion segmentation, according to pathological results. The proposed cascaded model based on Res-UNet takes prostate MR images (T2WI+ADC or only ADC) as inputs and automatically segments the whole prostate gland, the anatomic zones, and the csPCa region step by step. The performance of the models was evaluated and compared with PI-RADS (version 2.1) assessment using sensitivity, specificity, accuracy, and Dice similarity coefficient (DSC) in the held-out test set. Results In the test set, the per-lesion sensitivity of the biparametric (ADC + T2WI) model, ADC model, and PI-RADS assessment were 95.5% (84/88), 94.3% (83/88), and 94.3% (83/88) respectively (all p > 0.05). Additionally, the mean DSC based on the csPCa lesions were 0.64 ± 0.24 and 0.66 ± 0.23 for the biparametric model and ADC model, respectively. The sensitivity, specificity, and accuracy of the biparametric model were 95.6% (108/113), 91.5% (665/727), and 92.0% (773/840) based on sextant, and were 98.6% (68/69), 64.8% (46/71), and 81.4% (114/140) based on patients. The biparametric model had a similar performance to PI-RADS assessment (p > 0.05) and had higher specificity than the ADC model (86.8% [631/727], p< 0.001) based on sextant. Conclusion The cascaded deep learning model trained with ADC and T2WI achieves good performance for automated csPCa detection and localization.
Collapse
Affiliation(s)
- Lina Zhu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ge Gao
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Yi Zhu
- Department of Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Chao Han
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiang Liu
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Derun Li
- Department of Urology, Peking University First Hospital, Beijing, China
| | - Weipeng Liu
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Jingyuan Zhang
- Department of Development and Research, Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, Beijing, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, Beijing, China
- *Correspondence: Xiaoying Wang,
| |
Collapse
|
9
|
The Diagnostic Value of PI-RADS v2.1 in Patients with a History of Transurethral Resection of the Prostate (TURP). Curr Oncol 2022; 29:6373-6382. [PMID: 36135071 PMCID: PMC9497547 DOI: 10.3390/curroncol29090502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/28/2022] [Accepted: 09/01/2022] [Indexed: 11/24/2022] Open
Abstract
To explore the diagnostic value of the Prostate Imaging−Reporting and Data System version 2.1 (PI-RADS v2.1) for clinically significant prostate cancer (CSPCa) in patients with a history of transurethral resection of the prostate (TURP), we conducted a retrospective study of 102 patients who underwent systematic prostate biopsies with TURP history. ROC analyses and logistic regression analyses were performed to demonstrate the diagnostic value of PI-RADS v2.1 and other clinical characteristics, including PSA and free/total PSA (F/T PSA). Of 102 patients, 43 were diagnosed with CSPCa. In ROC analysis, PSA, F/T PSA, and PI-RADS v2.1 demonstrated significant diagnostic value in detecting CSPCa in our cohort (AUC 0.710 (95%CI 0.608−0.812), AUC 0.768 (95%CI 0.676−0.860), AUC 0.777 (95%CI 0.688−0.867), respectively). Further, PI-RADS v2.1 scores of the peripheral and transitional zones were analyzed separately. In ROC analysis, PI-RADS v2.1 remained valuable in identifying peripheral-zone CSPCa (AUC 0.780 (95%CI 0.665−0.854; p < 0.001)) while having limited capability in distinguishing transitional zone lesions (AUC 0.533 (95%CI 0.410−0.557; p = 0.594)). PSA and F/T PSA retain significant diagnostic value for CSPCa in patients with TURP history. PI-RADS v2.1 is reliable for detecting peripheral-zone CSPCa but has limited diagnostic value when assessing transitional zone lesions.
Collapse
|
10
|
Quantib Prostate Compared to an Expert Radiologist for the Diagnosis of Prostate Cancer on mpMRI: A Single-Center Preliminary Study. Tomography 2022; 8:2010-2019. [PMID: 36006066 PMCID: PMC9415513 DOI: 10.3390/tomography8040168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 08/07/2022] [Accepted: 08/08/2022] [Indexed: 11/30/2022] Open
Abstract
Background: To evaluate the clinical utility of an Artificial Intelligence (AI) radiology solution, Quantib Prostate, for prostate cancer (PCa) lesions detection on multiparametric Magnetic Resonance Images (mpMRI). Methods: Prostate mpMRI exams of 108 patients were retrospectively studied. The diagnostic performance of an expert radiologist (>8 years of experience) and of an inexperienced radiologist aided by Quantib software were compared. Three groups of patients were assessed: patients with positive mpMRI, positive target biopsy, and/or at least one positive random biopsy (group A, 73 patients); patients with positive mpMRI and a negative biopsy (group B, 14 patients), and patients with negative mpMRI who did not undergo biopsy (group-C, 21 patients). Results: In group A, the AI-assisted radiologist found new lesions with positive biopsy correlation, increasing the diagnostic PCa performance when compared with the expert radiologist, reaching an SE of 92.3% and a PPV of 90.1% (vs. 71.7% and 84.4%). In group A, the expert radiologist found 96 lesions on 73 mpMRI exams (17.7% PIRADS3, 56.3% PIRADS4, and 26% PIRADS5). The AI-assisted radiologist found 121 lesions (0.8% PIRADS3, 53.7% PIRADS4, and 45.5% PIRADS5). At biopsy, 33.9% of the lesions were ISUP1, 31.4% were ISUP2, 22% were ISUP3, 10.2% were ISUP4, and 2.5% were ISUP5. In group B, where biopsies were negative, the AI-assisted radiologist excluded three lesions but confirmed all the others. In group-C, the AI-assisted radiologist found 37 new lesions, most of them PIRADS 3, with 32.4% localized in the peripherical zone and 67.6% in the transition zone. Conclusions: Quantib software is a very sensitive tool to use specifically in high-risk patients (high PIRADS and high Gleason score).
Collapse
|
11
|
O'Shea A, Harisinghani M. PI-RADS: multiparametric MRI in prostate cancer. MAGMA (NEW YORK, N.Y.) 2022; 35:523-532. [PMID: 35596009 DOI: 10.1007/s10334-022-01019-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/28/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
Multiparametric MRI of the prostate gland has become the initial evaluation of biopsy naïve men with a clinical suspicion for prostate cancer. PI-RADS 2.1 is a joint initiative and framework for prostate MRI acquisition and reporting, which aims to standardize technique and interpretation across centers. Building upon experience accrued following the introduction of PI-RADS 2.0, version 2.1 provides key updates and important clarifications, although it is intended to be an active document, which continues to be updated. Continued advances in our understanding of prostate cancer and progress in imaging technology will undoubtedly shape future iterations of the reporting system.
Collapse
Affiliation(s)
- Aileen O'Shea
- Department of Radiology, 55 Fruit Street, Boston, MA, 02115, USA.
| | | |
Collapse
|
12
|
Beetz NL, Haas M, Baur A, Konietschke F, Roy A, Hamm CA, Rudolph MM, Shnayien S, Hamm B, Cash H, Asbach P, Penzkofer T. Inter-Reader Variability Using PI-RADS v2 Versus PI-RADS v2.1: Most New Disagreement Stems from Scores 1 and 2. ROFO-FORTSCHR RONTG 2022; 194:852-861. [PMID: 35545106 DOI: 10.1055/a-1752-1038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
PURPOSE To analyze possible differences in the inter-reader variability between PI-RADS version 2 (v2) and version 2.1 (v2.1) for the classification of prostate lesions using multiparametric MRI (mpMRI) of the prostate. METHODS In this retrospective and randomized study, 239 annotated and histopathologically correlated prostate lesions (104 positive and 135 negative for prostate cancer) were rated twice by three experienced uroradiologists using PI-RADS v2 and v2.1 with an interval of at least two months between readings. Results were tabulated across readers and reading timepoints and inter-reader variability was determined using Fleiss' kappa (κ). Thereafter, an additional analysis of the data was performed in which PI-RADS scores 1 and 2 were combined, as they have the same clinical consequences. RESULTS PI-PI-RADS v2.1 showed better inter-reader agreement in the peripheral zone (PZ), but poorer inter-reader agreement in the transition zone (TZ) (PZ: κ = 0.63 vs. κ = 0.58; TZ: κ = 0.47 vs. κ = 0.57). When PI-RADS scores 1 and 2 were combined, the use of PI-RADS v2.1 resulted in almost perfect inter-reader agreement in the PZ and substantial agreement in the TZ (PZ: κ = 0.81; TZ: κ = 0.80). CONCLUSION PI-RADS v2.1 improves inter-reader agreement in the PZ. New differences in inter-reader agreement were mainly the result of the assignment of PI-RADS v2.1 scores 1 and 2 to lesions in the TZ. Combining scores 1 and 2 improved inter-reader agreement both in the TZ and in the PZ, indicating that refined definitions may be warranted for these PI-RADS scores. KEY POINTS · PI-RADSv2.1 improves inter-reader agreement in the PZ but not in the TZ.. · New differences derived from PI-RADSv2.1 scores 1 and 2 in the TZ.. · Combined PI-RADSv2.1 scores of 1 and 2 yielded better inter-reader agreement.. · PI-RADSv2.1 appears to provide more precise description of lesions in the PZ.. · Improved inter-reader agreement in the PZ stresses the importance of appropriate lexicon description.. CITATION FORMAT · Beetz N, Haas M, Baur A et al. Inter-Reader Variability Using PI-RADS v2 Versus PI-RADS v2.1: Most New Disagreement Stems from Scores 1 and 2. Fortschr Röntgenstr 2022; 194: 852 - 861.
Collapse
Affiliation(s)
- Nick Lasse Beetz
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Matthias Haas
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Alexander Baur
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Frank Konietschke
- Department of Biometry and Clinical Epidemiology, Charite University Hospital Berlin, Germany
| | - Akash Roy
- Biostatistics and Bioinformatics, Duke University School of Medicine, DURHAM, United States
| | | | | | - Seyd Shnayien
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Hannes Cash
- Department of Urology, Charite University Hospital Berlin, Germany
| | - Patrick Asbach
- Department of Radiology, Charite University Hospital Berlin, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charite University Hospital Berlin, Germany
| |
Collapse
|
13
|
Meza J, Babajide R, Saoud R, Sweis J, Abelleira J, Helenowski I, Jovanovic B, Eggener S, Miller FH, Horowitz JM, Casalino DD, Murphy AB. Assessing the accuracy of multiparametric MRI to predict clinically significant prostate cancer in biopsy naïve men across racial/ethnic groups. BMC Urol 2022; 22:107. [PMID: 35850677 PMCID: PMC9295380 DOI: 10.1186/s12894-022-01066-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Introduction The Prostate Imaging Reporting and Data System (PIRADS) has shown promise in improving the detection of Gleason grade group (GG) 2–5 prostate cancer (PCa) and reducing the detection of indolent GG1 PCa. However, data on the performance of PIRADS in Black and Hispanic men is sparse. We evaluated the accuracy of PIRADS scores in detecting GG2-5 PCa in White, Black, and Hispanic men. Methods We performed a multicenter retrospective review of biopsy-naïve Black (n = 108), White (n = 108), and Hispanic (n = 64) men who underwent prostate biopsy (PB) following multiparametric MRI. Sensitivity and specificity of PIRADS for GG2-5 PCa were calculated. Race-stratified binary logistic regression models for GG2-5 PCa using standard clinical variables and PIRADS were used to calculate area under the receiver operating characteristics curves (AUC). Results Rates of GG2-5 PCa were statistically similar between Blacks, Whites, and Hispanics (52.8% vs 42.6% vs 37.5% respectively, p = 0.12). Sensitivity was lower in Hispanic men compared to White men (87.5% vs 97.8% respectively, p = 0.01). Specificity was similar in Black versus White men (21.6% vs 27.4%, p = 0.32) and White versus Hispanic men (27.4% vs 17.5%, p = 0.14). The AUCs of the PIRADS added to standard clinical data (age, PSA and suspicious prostate exam) were similar when comparing Black versus White men (0.75 vs 0.73, p = 0.79) and White versus Hispanic men (0.73 vs 0.59, p = 0.11). The AUCs for the Base model and PIRADS model alone were statistically similar when comparing Black versus White men and White versus Hispanic men. Conclusions The accuracy of the PIRADS and clinical data for detecting GG2-5 PCa seems statistically similar across race. However, there is concern that PIRADS 2.0 has lower sensitivity in Hispanic men compared to White men. Prospective validation studies are needed. Supplementary Information The online version contains supplementary material available at 10.1186/s12894-022-01066-9.
Collapse
Affiliation(s)
- Julio Meza
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA.
| | | | - Ragheed Saoud
- Arthur Smith Institute of Urology at Riverhead, Northwell Health, Riverhead, NY, USA
| | - Jamila Sweis
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA
| | - Josephine Abelleira
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA
| | - Irene Helenowski
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA
| | - Borko Jovanovic
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA
| | - Scott Eggener
- University of Chicago Division of Urology, Chicago, IL, USA
| | - Frank H Miller
- Northwestern University Department of Radiology, Chicago, IL, USA
| | | | - David D Casalino
- Northwestern University Department of Radiology, Chicago, IL, USA
| | - Adam B Murphy
- Department of Urology, Northwestern University, 710 N. Fairbanks Court Olson Pavilion 8-250, Chicago, IL, 60611, USA
| |
Collapse
|
14
|
Wen J, Tang T, Ji Y, Zhang Y. PI-RADS v2.1 Combined With Prostate-Specific Antigen Density for Detection of Prostate Cancer in Peripheral Zone. Front Oncol 2022; 12:861928. [PMID: 35463349 PMCID: PMC9024291 DOI: 10.3389/fonc.2022.861928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To evaluate the diagnostic performance of combining the Prostate Imaging Reporting and Data System (PI-RADS) scoring system v2.1 with prostate-specific antigen density (PSAD) to detect prostate cancer (PCa). Methods A total of 266 participants with suspicion of PCa underwent multiparametric magnetic resonance imaging (mpMRI) in our hospital, after at least 4 weeks all patients underwent subsequent systematic transrectal ultrasound (TRUS)-guided biopsy or MRI-TRUS fusion targeted biopsy. All mpMRI images were scored in accordance with the PI-RADS v2.1, and univariate and multivariate logistic regression analyses were performed to determine significant predictors of PCa. Results A total of 119 patients were diagnosed with PCa in the biopsy, of them 101 patients were diagnosed with clinically significant PCa. The multivariate analysis revealed that PI-RADS v2.1 and PSAD were independent predictors for PCa. For peripheral zone (PZ), the area under the ROC curve (AUC) for the combination of PI-RADS score and PSAD was 0.90 (95% CI 0.83-0.96), which is significantly superior to using PI-RADS score (0.85, 95% CI 0.78-0.93, P=0.031) and PSAD alone (0.83, 95% CI 0.75-0.90, P=0.037). For transition zone (TZ), however, the combination model was not significantly superior to PI-RADS alone, with AUC of 0.94 (95% CI 0.89-0.99) vs. 0.93 (95% CI 0.88-0.97, P=0.186). Conclusion The combination of PI-RADS v2.1 with PSAD could significantly improve the diagnostic performance of PCa in PZ. Nevertheless, no significant improvement was observed regarding PCa in TZ.
Collapse
Affiliation(s)
- Jing Wen
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Tingting Tang
- Department of Radiology, Yancheng First Peoples' Hospital, Yancheng, China
| | - Yugang Ji
- Department of Radiology, Yancheng First Peoples' Hospital, Yancheng, China
| | - Yilan Zhang
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
| |
Collapse
|
15
|
Raczeck P, Frenzel F, Woerner T, Graeber S, Bohle RM, Ziegler G, Buecker A, Schneider GK. Noninferiority of Monoparametric MRI Versus Multiparametric MRI for the Detection of Prostate Cancer: Diagnostic Accuracy of ADC Ratios Based on Advanced "Zoomed" Diffusion-Weighted Imaging. Invest Radiol 2022; 57:233-241. [PMID: 34743133 DOI: 10.1097/rli.0000000000000830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
OBJECTIVES The aim of this study was to compare the diagnostic accuracy of apparent diffusion coefficient (ADC) ratios as a monoparametric magnetic resonance imaging (MRI) protocol for the detection of prostate cancer (PCa) with the established multiparametric (mp) MRI at 3.0 T. MATERIALS AND METHODS According to power analysis, 52 male patients were included in this monocenter study with prospective data collection and retrospective, blinded multireader image analysis. The study was approved by the local ethics committee. Patients were recruited from January to December 2020. Based on mpMRI findings, patients underwent in-bore MR biopsy or prostatectomy for histopathologic correlation of suspicious lesions. Three readers, blinded to the histopathologic results and images of mpMRI, independently evaluated ADC maps for the detection of PCa. The ADC ratio was defined as the lowest signal intensity (SI) of lesions divided by the SI of normal tissue in the zone of origin. Predictive accuracy of multiparametric and monoparametric MRI were compared using logistic regression analysis. Moreover, both protocols were compared applying goodness-of-fit analysis with the Hosmer-Lemeshow test for continuous ADC ratios and Pearson χ2 test for binary decision calls, correlation analysis with Spearman ρ and intraclass correlation coefficients, as well as noninferiority assessment with a TOST ("two one-sided test"). RESULTS Eighty-one histopathologically proven, unique PCa lesions (Gleason score [GS] ≥ 3 + 3) in 52 patients could be unequivocally correlated, with 57 clinically significant (cs) PCa lesions (GS ≥ 3 + 4). Multiparametric MRI detected 95%, and monoparametric ADC detected ratios 91% to 93% of csPCa. Noninferiority of monoparametric MRI was confirmed by TOST (P < 0.05 for all comparisons). Logistic regression analysis revealed comparable predictive diagnostic accuracy of ADC ratios (73.7%-87.8%) versus mpMRI (72.2%-84.7%). Spearman rank correlation coefficient for PCa aggressiveness revealed satisfactory correlation of ADC ratios (P < 0.013 for all correlations). The Hosmer-Lemeshow test for the logistic regression analysis for continuous ADC ratios indicated adequate predictive accuracy (P = 0.55-0.87), and the Pearson χ2 test showed satisfactory goodness of fit (P = 0.35-0.69, χ2 = 0.16-0.87). CONCLUSIONS Normalized ADC ratios based on advanced DWI are noninferior to mpMRI at 3.0 T for the detection of csPCa in a preselected patient cohort and proved a fast and accurate assessment tool, thus showing a potential prospect of easing the development of future screening methods for PCa.
Collapse
Affiliation(s)
- Paul Raczeck
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| | - Felix Frenzel
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| | - Tobias Woerner
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| | - Stefan Graeber
- Institute of Medical Biometry, Epidemiology, and Medical Informatics, Saarland University, Campus Homburg
| | - Rainer M Bohle
- Institute of Pathology, Saarland University Medical Center, Homburg, Saarland, Germany
| | - Gesa Ziegler
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| | - Arno Buecker
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| | - Guenther K Schneider
- From the Clinic of Diagnostic and Interventional Radiology, Saarland University Medical Center
| |
Collapse
|
16
|
Urase Y, Ueno Y, Tamada T, Sofue K, Takahashi S, Hinata N, Harada K, Fujisawa M, Murakami T. Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer. Br J Radiol 2022; 95:20201434. [PMID: 33882243 PMCID: PMC8978254 DOI: 10.1259/bjr.20201434] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.1, in comparison with v. 2. METHODS Institutional review board approval was obtained for this retrospective study. 77 consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v. 2.1 and v. 2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated. RESULTS 228 regions were pathologically diagnosed as positive for csPC. With a cut-off ≥3, the agreement among all readers was better with v. 2.1 than v. 2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cut-off ≥4, the agreement among all readers was also better with v. 2.1 than v. 2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v. 2.1 was higher than with v. 2 (TZ, 0.826-0.907 vs 0.788-0.856; PZ, 0.857-0.919 vs 0.853-0.902). CONCLUSION Our study suggests that the PI-RADS v. 2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v. 2. ADVANCES IN KNOWLEDGE PI-RADS v. 2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.
Collapse
Affiliation(s)
- Yasuyo Urase
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tsutomu Tamada
- Departmentof Radiology, Kawasaki Medical School, Kurashiki, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Nobuyuki Hinata
- Department of Urology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Kenichi Harada
- Department of Urology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Masato Fujisawa
- Department of Urology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| |
Collapse
|
17
|
Lee CH, Vellayappan B, Tan CH. Comparison of diagnostic performance and inter-reader agreement between PI-RADS v2.1 and PI-RADS v2: systematic review and meta-analysis. Br J Radiol 2022; 95:20210509. [PMID: 34520694 PMCID: PMC8978226 DOI: 10.1259/bjr.20210509] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES To perform a systematic review and meta-analysis comparing diagnostic performance and inter reader agreement between PI-RADS v. 2.1 and PI-RADS v. 2 in the detection of clinically significant prostate cancer (csPCa). METHODS A systematic review was performed, searching the major biomedical databases (Medline, Embase, Scopus), using the keywords "PIRADS 2.1" or "PI RADS 2.1" or "PI-RADS 2.1". Studies reporting on head-to-head diagnostic comparison between PI-RADS v. 2.1 and v. 2 were included. Pooled sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were compared between PI-RADS v. 2.1 and v. 2. Summary receiver operator characteristic graphs were plotted. Analysis was performed for whole gland, and pre-planned subgroup analysis was performed by tumour location (whole gland vs transition zone (TZ)), high b-value DWI (b-value ≥1400 s/mm2), and reader experience (<5 years vs ≥5 years with prostate MRI interpretation). Inter-reader agreement and pooled rates of csPCa for PI-RADS 1-3 lesions were compared between PI-RADS v. 2.1 and v. 2. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool v. 2 (QUADAS-2). RESULTS Eight studies (1836 patients, 1921 lesions) were included. Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for whole gland (0.62 vs 0.66, p = 0.02). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.17, 0.31, 0.41). Pooled specificity for PI-RADS v. 2.1 was significantly lower than PI-RADS v. 2 for TZ only (0.67 vs 0.72, p = 0.01). Pooled sensitivities, PPVs and NPVs were not significantly different (p = 0.06, 0.36, 0.17). Amongst studies utilising diffusion-weighted imaging with highest b-value of ≥1400 s/mm2, pooled sensitivities, specificities, PPVs and NPVs were not significantly different (p = 0.52, 0.4, 0.5, 0.47). There were no significant differences in pooled sensitivities, specificities, PPVs and NPVs between PI-RADS v. 2.1 and PI-RADS v. 2 for less-experienced readers (p = 0.65, 0.37, 0.65, 0.81) and for more experienced readers (p = 0.57, 0.90, 0.91, 0.65). For PI-RADS v. 2.1 alone, there were no significant differences in pooled sensitivity, specificity, PPV and NPV between less and more experienced readers (p = 0.38, 0.70, 1, 0.48). Inter-reader agreement was moderate to substantial for both PI-RADS v. 2.1 and v. 2. There were no significant differences between pooled csPCa rates between PI-RADS v. 2.1 and v. 2 for PI-RADS 1-2 lesions (6.6% vs 7.3%, p = 0.53), or PI-RADS 3 lesions (24.1% vs 26.8%, p = 0.28). CONCLUSIONS Diagnostic performance and inter-reader agreement for PI-RADS v. 2.1 is comparable to PI-RADS v. 2, however the significantly lower specificity of PI-RADS v. 2.1 may result in increased number of unnecessary biopsies. ADVANCES IN KNOWLEDGE 1. Compared to PI-RADS v. 2, PI-RADS v. 2.1 has a non-significantly higher sensitivity but a significantly lower specificity for detection of clinically significant prostate cancer.2. PI-RADS v. 2.1 could potentially result in considerable increase in number of negative targeted biopsy rates for PI-RADS 3 lesions, which could have been potentially avoided.
Collapse
Affiliation(s)
- Chau Hung Lee
- Department of Radiology, Tan Tock Seng Hospital, Singapore, Singapore
| | - Balamurugan Vellayappan
- Department of Radiation Oncology, National University Cancer Institute, National University Health System, Singapore, Singapore
| | | |
Collapse
|
18
|
Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review. Curr Urol Rep 2022; 23:1-10. [PMID: 35226257 DOI: 10.1007/s11934-022-01084-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW To present the latest evidence related to interobserver agreement and accuracy; evaluate the strengths, weaknesses, and implications of use; and outline opportunities for improvement and future development of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for detection of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI). RECENT FINDINGS Our review of currently available evidence suggests that recent improvements to the PI-RADS system with PI-RADS v2.1 slightly improved interobserver agreement, with generally high sensitivity and moderate specificity for the detection of clinically significant PCa. Recent evidence additionally demonstrates substantial improvement in diagnostic specificity with PI-RADS v2.1 compared with PI-RADS v2. However, results of studies examining the comparative performance of v2.1 are limited by small sample sizes and retrospective cohorts, potentially introducing selection bias. Some studies suggest a substantial improvement between v2.1 and v2, while others report no statistically significant difference. Additionally, in PI-RADS v2.1, the interpretation and reporting of certain findings remain subjective, particularly for category 2 lesions, and reader experience continues to vary significantly. These factors further contribute to a remaining degree of interobserver variability and findings of improved performance among more experienced readers. PI-RADS v2.1 appears to show at least minimal improvement in interobserver agreement, diagnostic performance, and both sensitivity and specificity, with greater improvements seen among more experienced readers. However, given the decrescent nature of these improvements and the limited power of all studies examined, the clinical impact of this progress may be marginal. Despite improvements in PI-RADS v2.1, practitioner experience in interpreting mpMRI of the prostate remains the most important factor in prostate cancer detection.
Collapse
|
19
|
Li D, Han X, Gao J, Zhang Q, Yang H, Liao S, Guo H, Zhang B. Deep Learning in Prostate Cancer Diagnosis Using Multiparametric Magnetic Resonance Imaging With Whole-Mount Histopathology Referenced Delineations. Front Med (Lausanne) 2022; 8:810995. [PMID: 35096899 PMCID: PMC8793798 DOI: 10.3389/fmed.2021.810995] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Multiparametric magnetic resonance imaging (mpMRI) plays an important role in the diagnosis of prostate cancer (PCa) in the current clinical setting. However, the performance of mpMRI usually varies based on the experience of the radiologists at different levels; thus, the demand for MRI interpretation warrants further analysis. In this study, we developed a deep learning (DL) model to improve PCa diagnostic ability using mpMRI and whole-mount histopathology data. Methods: A total of 739 patients, including 466 with PCa and 273 without PCa, were enrolled from January 2017 to December 2019. The mpMRI (T2 weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences) data were randomly divided into training (n = 659) and validation datasets (n = 80). According to the whole-mount histopathology, a DL model, including independent segmentation and classification networks, was developed to extract the gland and PCa area for PCa diagnosis. The area under the curve (AUC) were used to evaluate the performance of the prostate classification networks. The proposed DL model was subsequently used in clinical practice (independent test dataset; n = 200), and the PCa detective/diagnostic performance between the DL model and different level radiologists was evaluated based on the sensitivity, specificity, precision, and accuracy. Results: The AUC of the prostate classification network was 0.871 in the validation dataset, and it reached 0.797 using the DL model in the test dataset. Furthermore, the sensitivity, specificity, precision, and accuracy of the DL model for diagnosing PCa in the test dataset were 0.710, 0.690, 0.696, and 0.700, respectively. For the junior radiologist without and with DL model assistance, these values were 0.590, 0.700, 0.663, and 0.645 versus 0.790, 0.720, 0.738, and 0.755, respectively. For the senior radiologist, the values were 0.690, 0.770, 0.750, and 0.730 vs. 0.810, 0.840, 0.835, and 0.825, respectively. The diagnosis made with DL model assistance for radiologists were significantly higher than those without assistance (P < 0.05). Conclusion: The diagnostic performance of DL model is higher than that of junior radiologists and can improve PCa diagnostic accuracy in both junior and senior radiologists.
Collapse
Affiliation(s)
- Danyan Li
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaowei Han
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jie Gao
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Qing Zhang
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Haibo Yang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Shu Liao
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Hongqian Guo
- Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China.,Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| |
Collapse
|
20
|
Reiter R, Majumdar S, Kearney S, Kajdacsy-Balla A, Macias V, Crivellaro S, Abern M, Royston TJ, Klatt D. Investigating the heterogeneity of viscoelastic properties in prostate cancer using MR elastography at 9.4T in fresh prostatectomy specimens. Magn Reson Imaging 2022; 87:113-118. [PMID: 35007693 DOI: 10.1016/j.mri.2022.01.005] [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: 10/21/2021] [Revised: 12/29/2021] [Accepted: 01/04/2022] [Indexed: 11/20/2022]
Abstract
PURPOSE To quantify the heterogeneity of viscoelastic tissue properties in prostatectomy specimens from men with prostate cancer (PC) using MR elastography (MRE) with histopathology as reference. METHODS Twelve fresh prostatectomy specimens were examined in a preclinical 9.4T MRI scanner. Maps of the complex shear modulus (|G*| in kPa) with its real and imaginary part (G' and G" in kPa) were calculated at 500 Hz. Prostates were divided into 12 segments for segment-wise measurement of viscoelastic properties and histopathology. Coefficients of variation (CVs in %) were calculated for quantification of heterogeneity. RESULTS Group-averaged values of cancerous vs. benign segments were significantly increased: |G*| of 12.13 kPa vs. 6.14 kPa, G' of 10.84 kPa vs. 5.44 kPa and G" of 5.45 kPa vs. 2.92 kPa, all p < 0.001. In contrast, CVs were significantly increased for benign segments: 23.59% vs. 26.32% (p = 0.014) for |G*|, 27.05% vs. 37.84% (p < 0.003) for G', and 36.51% vs. 50.37% (p = 0.008) for G". DISCUSSION PC is characterized by a stiff yet homogeneous biomechanical signature, which may be due to the unique nondestructive growth pattern of PC with intervening stroma, providing a rigid scaffold in the affected area. In turn, increased heterogeneity in benign prostate segments may be attributable to the presence of different prostate zones with involvement by specific nonmalignant pathology.
Collapse
Affiliation(s)
- Rolf Reiter
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178 Berlin, Germany; Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Shreyan Majumdar
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Steven Kearney
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States
| | - André Kajdacsy-Balla
- Department of Pathology, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Virgilia Macias
- Department of Pathology, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Simone Crivellaro
- Department of Urology, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Michael Abern
- Department of Urology, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Thomas J Royston
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| | - Dieter Klatt
- Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago, 830 South Wood Street, Chicago, IL 60612, United States.
| |
Collapse
|
21
|
Lim CS, Abreu-Gomez J, Thornhill R, James N, Al Kindi A, Lim AS, Schieda N. Utility of machine learning of apparent diffusion coefficient (ADC) and T2-weighted (T2W) radiomic features in PI-RADS version 2.1 category 3 lesions to predict prostate cancer diagnosis. Abdom Radiol (NY) 2021; 46:5647-5658. [PMID: 34467426 DOI: 10.1007/s00261-021-03235-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/31/2021] [Accepted: 07/31/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To evaluate if machine learning (ML) of radiomic features extracted from apparent diffusion coefficient (ADC) and T2-weighted (T2W) MRI can predict prostate cancer (PCa) diagnosis in Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 category 3 lesions. METHODS This multi-institutional review board-approved retrospective case-control study evaluated 158 men with 160 PI-RADS category 3 lesions (79 peripheral zone, 81 transition zone) diagnosed at 3-Tesla MRI with histopathology diagnosis by MRI-TRUS-guided targeted biopsy. A blinded radiologist confirmed PI-RADS v2.1 score and segmented lesions on axial T2W and ADC images using 3D Slicer, extracting radiomic features with an open-source software (Pyradiomics). Diagnostic accuracy for (1) any PCa and (2) clinically significant (CS; International Society of Urogenital Pathology Grade Group ≥ 2) PCa was assessed using XGBoost with tenfold cross -validation. RESULTS From 160 PI-RADS 3 lesions, there were 50.0% (80/160) PCa, including 36.3% (29/80) CS-PCa (63.8% [51/80] ISUP 1, 23.8% [19/80] ISUP 2, 8.8% [7/80] ISUP 3, 3.8% [3/80] ISUP 4). The remaining 50.0% (80/160) lesions were benign. ML of all radiomic features from T2W and ADC achieved area under receiver operating characteristic curve (AUC) for diagnosis of (1) CS-PCa 0.547 (95% Confidence Intervals 0.510-0.584) for T2W and 0.684 (CI 0.652-0.715) for ADC and (2) any PCa 0.608 (CI 0.579-0.636) for T2W and 0.642 (CI 0.614-0.0.670) for ADC. CONCLUSION Our results indicate ML of radiomic features extracted from T2W and ADC achieved at best moderate accuracy for determining which PI-RADS category 3 lesions represent PCa.
Collapse
Affiliation(s)
- Christopher S Lim
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Rm AB 279, Toronto, ON, M4N 3M5, Canada.
| | - Jorge Abreu-Gomez
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
- Department of Medical Imaging, Joint Department of Medical Imaging, University of Toronto, 585 University Avenue PMB-298, Toronto, ON, M5G2N2, Canada
| | - Rebecca Thornhill
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, 1053 Carling Ave, Civic Campus C1, Ottawa, ON, K1Y 4E9, Canada
| | - Nick James
- Software Solutions, The Ottawa Hospital, Ottawa, Canada
| | - Ahmed Al Kindi
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, 2075 Bayview Avenue, Rm AB 279, Toronto, ON, M4N 3M5, Canada
| | - Andrew S Lim
- Department of Radiation Oncology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Ave. E, Seattle Washington, 98109-1023, USA
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, The University of Ottawa, 1053 Carling Ave, Civic Campus C1, Ottawa, ON, K1Y 4E9, Canada
| |
Collapse
|
22
|
Oerther B, Engel H, Bamberg F, Sigle A, Gratzke C, Benndorf M. Cancer detection rates of the PI-RADSv2.1 assessment categories: systematic review and meta-analysis on lesion level and patient level. Prostate Cancer Prostatic Dis 2021; 25:256-263. [PMID: 34230616 PMCID: PMC9184264 DOI: 10.1038/s41391-021-00417-1] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 06/05/2021] [Accepted: 06/22/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND The Prostate Imaging Reporting and Data System, version 2.1 (PI-RADSv2.1) standardizes reporting of multiparametric MRI of the prostate. Assigned assessment categories are a risk stratification algorithm, higher categories indicate a higher probability of clinically significant cancer compared to lower categories. PI-RADSv2.1 does not define these probabilities numerically. We conduct a systematic review and meta-analysis to determine the cancer detection rates (CDR) of the PI-RADSv2.1 assessment categories on lesion level and patient level. METHODS Two independent reviewers screen a systematic PubMed and Cochrane CENTRAL search for relevant articles (primary outcome: clinically significant cancer, index test: prostate MRI reading according to PI-RADSv2.1, reference standard: histopathology). We perform meta-analyses of proportions with random-effects models for the CDR of the PI-RADSv2.1 assessment categories for clinically significant cancer. We perform subgroup analysis according to lesion localization to test for differences of CDR between peripheral zone lesions and transition zone lesions. RESULTS A total of 17 articles meet the inclusion criteria and data is independently extracted by two reviewers. Lesion level analysis includes 1946 lesions, patient level analysis includes 1268 patients. On lesion level analysis, CDR are 2% (95% confidence interval: 0-8%) for PI-RADS 1, 4% (1-9%) for PI-RADS 2, 20% (13-27%) for PI-RADS 3, 52% (43-61%) for PI-RADS 4, 89% (76-97%) for PI-RADS 5. On patient level analysis, CDR are 6% (0-20%) for PI-RADS 1, 9% (5-13%) for PI-RADS 2, 16% (7-27%) for PI-RADS 3, 59% (39-78%) for PI-RADS 4, 85% (73-94%) for PI-RADS 5. Higher categories are significantly associated with higher CDR (P < 0.001, univariate meta-regression), no systematic difference of CDR between peripheral zone lesions and transition zone lesions is identified in subgroup analysis. CONCLUSIONS Our estimates of CDR demonstrate that PI-RADSv2.1 stratifies lesions and patients as intended. Our results might serve as an initial evidence base to discuss management strategies linked to assessment categories.
Collapse
Affiliation(s)
- Benedict Oerther
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany
| | - Hannes Engel
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany
| | - August Sigle
- Department of Urology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany
| | - Christian Gratzke
- Department of Urology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, Freiburg, Germany.
| |
Collapse
|
23
|
Wujciak D, Antoch G. [Financing perspectives for multiparametric magnetic resonance prostatography]. Radiologe 2021; 61:825-828. [PMID: 34213621 DOI: 10.1007/s00117-021-00867-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Not only is the evidence for multiparametric magnetic resonance prostatography clearly proven based on current research, the S3 guideline for prostate cancer recommends its use prior to invasive biopsy. Remuneration through the GKV does not occur. OBJECTIVES The negotiations concerning the inclusion in the EBM (German Uniform Evaluation Standard) Catalogue of statutory health insurance funds take place in a highly politicized environment and under economic priorities. The routes that are possible in the complex registration procedure are described. MATERIALS AND METHODS Radiology associations (Berufsverband der Deutschen Radiologen [BDR] und Deutsche Röntgengesellschaft [DRG]) have supported their methods with evidence and quality assurance. Special contracts with health insurance funds, coordinated at the level of the federal states, pave the way and accelerate accreditation. RESULTS The definition of the service according to the EBM, the recommendation concerning remuneration as well as supporting documents and a functional quality assurance system have been made available to the Joint Valuation Committee of physicians & health insurance funds as part of the application for approval. CONCLUSIONS Due to the nature of the system, the presented evidence and quality assurance, as well as the development of special contracts, have inevitably been transferred to radiology and the unified work of their associations. The imaging modality prostatography shows the advancement of radiological methods for dedicated multiparametric organ diagnostics.
Collapse
Affiliation(s)
- Detlef Wujciak
- Radiologische Praxis Halle, Niemeyerstraße 23, 06110, Halle/ Saale, Deutschland.
| | - Gerald Antoch
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsklinikum Düsseldorf, Düsseldorf, Deutschland
| |
Collapse
|
24
|
Hötker A, Donati OF. [PI-RADS 2.1 and structured reporting of magnetic resonance imaging of the prostate]. Radiologe 2021; 61:802-809. [PMID: 34213622 PMCID: PMC8410719 DOI: 10.1007/s00117-021-00868-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 11/30/2022]
Abstract
Klinisches/methodisches Problem Die Identifikation klinisch signifikanter Prostatakarzinome bei gleichzeitigem Vermeiden einer Überdiagnostik niedrigmaligner Tumoren stellt eine Herausforderung in der klinischen Routine dar. Radiologische Standardverfahren Die gemäß PI-RADS-Richtlinien (Prostate Imaging Reporting and Data System Guidelines) akquirierte und interpretierte multiparametrische Magnetresonanztomographie (MRT) der Prostata ist als klinischer Standard bei Urologen und Radiologen akzeptiert. Methodische Innovationen Die PI-RADS-Richtlinien sind neu auf Version 2.1 aktualisiert worden und beinhalten neben präzisierten technischen Anforderungen einzelne Änderungen in der Läsionsbewertung. Leistungsfähigkeit Die PI-RADS-Richtlinien haben entscheidende Bedeutung in der Standardisierung der multiparametrischen MRT der Prostata erlangt und bieten Vorlagen zur strukturierten Befundung, was die Kommunikation mit dem Zuweiser erleichtert. Bewertung Die nun auf Version 2.1 aktualisierten Richtlinien stellen eine Verfeinerung der verbreiteten Version 2.0 dar. Dabei wurden viele Aspekte der Befundung präzisiert, einige vorbekannte Limitationen bleiben jedoch bestehen und erfordern die weitere Verbesserung der Richtlinien in kommenden Versionen.
Collapse
Affiliation(s)
- Andreas Hötker
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsspital Zürich, Rämistrasse 100, 8091, Zürich, Schweiz
| | - Olivio F Donati
- Institut für Diagnostische und Interventionelle Radiologie, Universitätsspital Zürich, Rämistrasse 100, 8091, Zürich, Schweiz.
| |
Collapse
|
25
|
Twilt JJ, van Leeuwen KG, Huisman HJ, Fütterer JJ, de Rooij M. Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11060959. [PMID: 34073627 PMCID: PMC8229869 DOI: 10.3390/diagnostics11060959] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/14/2022] Open
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
Collapse
|
26
|
Pecoraro M, Messina E, Bicchetti M, Carnicelli G, Del Monte M, Iorio B, La Torre G, Catalano C, Panebianco V. The future direction of imaging in prostate cancer: MRI with or without contrast injection. Andrology 2021; 9:1429-1443. [PMID: 33998173 DOI: 10.1111/andr.13041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/23/2021] [Accepted: 05/05/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Multiparametric MRI (mpMRI) is the "state of the art" management tool for patients with suspicion of prostate cancer (PCa). The role of non-contrast MRI is investigated to move toward a more personalized, less invasive, and highly cost-effective PCa diagnostic workup. OBJECTIVE To perform a non-systematic review of the existing literature to highlight strength and flaws of performing non-contrast MRI, and to provide a critical overview of the international scientific production on the topic. MATERIALS AND METHODS Online databases (Medline, PubMed, and Web of Science) were searched for original articles, systematic review and meta-analysis, and expert opinion papers. RESULTS Several investigations have shown comparable diagnostic accuracy of biparametric (bpMRI) and mpMRI for the detection of PCa. The advantage of abandoning contrast-enhanced sequences improves operational logistics, lowering costs, acquisition time, and side effects. The main limitations of bpMRI are that most studies comparing non-contrast with contrast MRI come from centers with high expertise that might not be reproducible in the general community setting; besides, reduced protocols might be insufficient for estimation of the intra- and extra-prostatic extension and regional disease. The mentioned observations suggest that low-quality mpMRI for the general population might represent the main shortage to overcome. DISCUSSION Non-contrast MRI future trends are likely represented by PCa screening and the application of artificial intelligence (AI) tools. PCa screening is still a controversial topic; bpMRI has become one of the most promising diagnostic applications, as it is a more sensitive test for PCa early detection, compared to serum PSA level test. Also, AI applications and radiomic have been the object of several studies investigating PCa detection using bpMRI, showing encouraging results. CONCLUSION Today, the accessibility to MRI for early detection of PCa is a priority. Results from prospective, multicenter, multireader, and paired validation studies are needed to provide evidence supporting its role in the clinical practice.
Collapse
Affiliation(s)
- Martina Pecoraro
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Emanuele Messina
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Marco Bicchetti
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Giorgia Carnicelli
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Maurizio Del Monte
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Beniamino Iorio
- Department of Surgical Sciences, "Tor Vergata" University of Rome, Rome, Italy
| | - Giuseppe La Torre
- Department of Public Health and Infectious Disease, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Carlo Catalano
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| |
Collapse
|