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Oerther B, Nedelcu A, Engel H, Schmucker C, Schwarzer G, Brugger T, Schoots IG, Eisenblaetter M, Sigle A, Gratzke C, Bamberg F, Benndorf M. Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis. Radiology 2024; 312:e233337. [PMID: 39136561 DOI: 10.1148/radiol.233337] [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: 08/29/2024]
Abstract
Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.
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Affiliation(s)
- Benedict Oerther
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Andrea Nedelcu
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Hannes Engel
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Christine Schmucker
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Guido Schwarzer
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Timo Brugger
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Ivo G Schoots
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Michel Eisenblaetter
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - August Sigle
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Christian Gratzke
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Fabian Bamberg
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
| | - Matthias Benndorf
- From the Department of Radiology (B.O., A.N., H.E., F.B., M.B.), Institute for Evidence in Medicine (C.S., T.B.), Institute of Medical Biometry and Statistics (G.S.), Department of Urology (A.S., C.G.), and Berta-Ottenstein-Programme (A.S), Faculty of Medicine, University of Freiburg Medical Center, Freiburg, Germany; Department of Radiology & Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands (I.G.S); and Department of Diagnostic and Interventional Radiology, Medical Faculty OWL, University of Bielefeld, Klinikum Lippe, Röntgenstrasse 18, 32756 Detmold, Germany (M.E., M.B.)
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Kohjimoto Y, Uemura H, Yoshida M, Hinotsu S, Takahashi S, Takeuchi T, Suzuki K, Shinmoto H, Tamada T, Inoue T, Sugimoto M, Takenaka A, Habuchi T, Ishikawa H, Mizowaki T, Saito S, Miyake H, Matsubara N, Nonomura N, Sakai H, Ito A, Ukimura O, Matsuyama H, Hara I. Japanese clinical practice guidelines for prostate cancer 2023. Int J Urol 2024. [PMID: 39078210 DOI: 10.1111/iju.15545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Accepted: 07/09/2024] [Indexed: 07/31/2024]
Abstract
This fourth edition of the Japanese Clinical Practice Guidelines for Prostate Cancer 2023 is compiled. It was revised under the leadership of the Japanese Urological Association, with members selected from multiple academic societies and related organizations (Japan Radiological Society, Japanese Society for Radiation Oncology, the Department of EBM and guidelines, Japan Council for Quality Health Care (Minds), Japanese Society of Pathology, and the patient group (NPO Prostate Cancer Patients Association)), in accordance with the Minds Manual for Guideline Development (2020 ver. 3.0). The most important feature of this revision is the adoption of systematic reviews (SRs) in determining recommendations for 14 clinical questions (CQs). Qualitative SRs for these questions were conducted, and the final recommendations were made based on the results through the votes of 24 members of the guideline development group. Five algorithms based on these results were also created. Contents not covered by the SRs, which are considered textbook material, have been described in the general statement. In the general statement, a literature search for 14 areas was conducted; then, based on the general statement and CQs of the Japanese Clinical Practice Guidelines for Prostate Cancer 2016, the findings revealed after the 2016 guidelines were mainly described. This article provides an overview of these guidelines.
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Affiliation(s)
- Yasuo Kohjimoto
- Department of Urology, Wakayama Medical University, Wakayama, Japan
| | - Hiroji Uemura
- Department of Urology and Renal Transplantation, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Masahiro Yoshida
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Narita, Chiba, Japan
- Department of EBM and Guidelines, Japan Council for Quality Health Care (Minds), Tokyo, Japan
| | - Shiro Hinotsu
- Department of Biostatistics and Data Management, Sapporo Medical University School of Medicine, Sapporo, Japan
| | - Satoru Takahashi
- Department of Urology, Nihon University School of Medicine, Tokyo, Japan
| | - Tsutomu Takeuchi
- NPO Prostate Cancer Patients Association, Takarazuka, Hyogo, Japan
| | - Kazuhiro Suzuki
- Department of Urology, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan
| | - Hiroshi Shinmoto
- Department of Radiology, National Defense Medical College, Tokorozawa, Tochigi, Japan
| | - Tsutomu Tamada
- Department of Radiology, Kawasaki Medical School, Kurashiki, Okayama, Japan
| | - Takahiro Inoue
- Department of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Tsu, Mie, Japan
| | - Mikio Sugimoto
- Department of Urology, Faculty of Medicine, Kagawa University, Takamatsu, Kagawa, Japan
| | - Atsushi Takenaka
- Division of Urology, Department of Surgery, Faculty of Medicine, Tottori University, Yonago, Tottori, Japan
| | - Tomonori Habuchi
- Department of Urology, Akita University Graduate School of Medicine, Akita, Japan
| | - Hitoshi Ishikawa
- QST Hospital, National Institutes for Quantum Science and Technology, Chiba, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shiro Saito
- Department of Urology, Prostate Cancer Center Ofuna Chuo Hospital, Kamakura, Kanagawa, Japan
| | - Hideaki Miyake
- Division of Urology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Nobuaki Matsubara
- Department of Medical Oncology, National Cancer Center Hospital East, Kashiwa, Chiba, Japan
| | - Norio Nonomura
- Department of Urology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hideki Sakai
- Department of Urology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
- Nagasaki Rosai Hospital, Sasebo, Nagasaki, Japan
| | - Akihiro Ito
- Department of Urology, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Osamu Ukimura
- Department of Urology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Hideyasu Matsuyama
- Department of Urology, Graduate School of Medicine, Yamaguchi University, Ube, Yamaguchi, Japan
- Department of Urology, JA Yamaguchi Kouseiren Nagato General Hospital, Yamaguchi, Japan
| | - Isao Hara
- Department of Urology, Wakayama Medical University, Wakayama, Japan
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Xinyang S, Tianci S, Xiangyu H, Shuang Z, Yangyang W, Mengying D, Tonghui X, Jingran Z, Feng Y. A semi-automatic deep learning model based on biparametric MRI scanning strategy to predict bone metastases in newly diagnosed prostate cancer patients. Front Oncol 2024; 14:1298516. [PMID: 38919538 PMCID: PMC11196796 DOI: 10.3389/fonc.2024.1298516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 05/29/2024] [Indexed: 06/27/2024] Open
Abstract
Objective To develop a semi-automatic model integrating radiomics, deep learning, and clinical features for Bone Metastasis (BM) prediction in prostate cancer (PCa) patients using Biparametric MRI (bpMRI) images. Methods A retrospective study included 414 PCa patients (BM, n=136; NO-BM, n=278) from two institutions (Center 1, n=318; Center 2, n=96) between January 2016 and December 2022. MRI scans were confirmed with BM status via PET-CT or ECT pre-treatment. Tumor areas on bpMRI images were delineated as tumor's region of interest (ROI) using auto-delineation tumor models, evaluated with Dice similarity coefficient (DSC). Samples were auto-sketched, refined, and used to train the ResNet BM prediction model. Clinical, radiomics, and deep learning data were synthesized into the ResNet-C model, evaluated using receiver operating characteristic (ROC). Results The auto-segmentation model achieved a DSC of 0.607. Clinical BM prediction's internal validation had an accuracy (ACC) of 0.650 and area under the curve (AUC) of 0.713; external cohort had an ACC of 0.668 and AUC of 0.757. The deep learning model yielded an ACC of 0.875 and AUC of 0.907 for the internal, and ACC of 0.833 and AUC of 0.862 for the external cohort. The Radiomics model registered an ACC of 0.819 and AUC of 0.852 internally, and ACC of 0.885 and AUC of 0.903 externally. ResNet-C demonstrated the highest ACC of 0.902 and AUC of 0.934 for the internal, and ACC of 0.885 and AUC of 0.903 for the external cohort. Conclusion The ResNet-C model, utilizing bpMRI scanning strategy, accurately assesses bone metastasis (BM) status in newly diagnosed prostate cancer (PCa) patients, facilitating precise treatment planning and improving patient prognoses.
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Affiliation(s)
- Song Xinyang
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
- Department of Radiology, The People’s Hospital of Zouping City, Zouping, China
| | - Shen Tianci
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Hu Xiangyu
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Zhang Shuang
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Wang Yangyang
- Department of Orthopedics, Xiangyang No. 1 People’s Hospital, Jinzhou Medical University Union Training Base, Xiangyang, China
| | - Du Mengying
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Xu Tonghui
- Department of Radiology, The People’s Hospital of Zouping City, Zouping, China
| | - Zhou Jingran
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
| | - Yang Feng
- Department of Radiology, Xiangyang No. 1 People’s Hospital, Hubei University of Medicine, Xiangyang, China
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Castaldo R, Brancato V, Cavaliere C, Pecchia L, Illiano E, Costantini E, Ragozzino A, Salvatore M, Nicolai E, Franzese M. Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters. Front Oncol 2024; 14:1323247. [PMID: 38873254 PMCID: PMC11171723 DOI: 10.3389/fonc.2024.1323247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/01/2024] [Indexed: 06/15/2024] Open
Abstract
Introduction Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Methods Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. Results The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. Discussion This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.
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Affiliation(s)
- Rossana Castaldo
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | | | - Carlo Cavaliere
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Leandro Pecchia
- School of Engineering, University of Warwick, Coventry, United Kingdom
- Università Campus Bio-Medico Roma, Roma, Italy
- Campus Bio-Medico, Fondazione Policlinico Universitario, Roma, Italy
| | - Ester Illiano
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Elisabetta Costantini
- Adrology and Urogynecological Clinic, Santa Maria Terni Hospital, University of Perugia, Terni, Italy
| | - Alfonso Ragozzino
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Marco Salvatore
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Emanuele Nicolai
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
| | - Monica Franzese
- Bioinformatics and Biostatistics Lab, IRCCS SYNLAB SDN, Naples, Italy
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Li S, Ye X, Tian H, Ding Z, Cui C, Shi S, Yang Y, Li G, Chen J, Lin Z, Ni Z, Xu J, Dong F. An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer. Postgrad Med J 2024; 100:228-236. [PMID: 38142286 DOI: 10.1093/postmj/qgad127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/25/2023] [Accepted: 11/10/2023] [Indexed: 12/25/2023]
Abstract
PURPOSE We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model. METHODS A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model. RESULTS In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655). CONCLUSION The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.
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Affiliation(s)
- Shiyu Li
- Department of Ultrasound, The Second Clinical Medical College of Jinan University, China
| | - Xiuqin Ye
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Hongtian Tian
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Zhimin Ding
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Chen Cui
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Siyuan Shi
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Yang Yang
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Guoqiu Li
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Jing Chen
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Ziwei Lin
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Zhipeng Ni
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Jinfeng Xu
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
| | - Fajin Dong
- Department of Ultrasound, The Second Clinical Medical College, Jinan University, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
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Zheng H, Hung ALY, Miao Q, Song W, Scalzo F, Raman SS, Zhao K, Sung K. AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI. Sci Rep 2024; 14:5740. [PMID: 38459100 PMCID: PMC10923873 DOI: 10.1038/s41598-024-56405-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 03/06/2024] [Indexed: 03/10/2024] Open
Abstract
Multi-parametric MRI (mpMRI) is widely used for prostate cancer (PCa) diagnosis. Deep learning models show good performance in detecting PCa on mpMRI, but domain-specific PCa-related anatomical information is sometimes overlooked and not fully explored even by state-of-the-art deep learning models, causing potential suboptimal performances in PCa detection. Symmetric-related anatomical information is commonly used when distinguishing PCa lesions from other visually similar but benign prostate tissue. In addition, different combinations of mpMRI findings are used for evaluating the aggressiveness of PCa for abnormal findings allocated in different prostate zones. In this study, we investigate these domain-specific anatomical properties in PCa diagnosis and how we can adopt them into the deep learning framework to improve the model's detection performance. We propose an anatomical-aware PCa detection Network (AtPCa-Net) for PCa detection on mpMRI. Experiments show that the AtPCa-Net can better utilize the anatomical-related information, and the proposed anatomical-aware designs help improve the overall model performance on both PCa detection and patient-level classification.
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Affiliation(s)
- Haoxin Zheng
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA.
- Computer Science, University of California, Los Angeles, Los Angeles, 90095, USA.
| | - Alex Ling Yu Hung
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA
- Computer Science, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Qi Miao
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Weinan Song
- Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Fabien Scalzo
- Computer Science, University of California, Los Angeles, Los Angeles, 90095, USA
- The Seaver College, Pepperdine University, Los Angeles, 90363, USA
| | - Steven S Raman
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Kai Zhao
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA
| | - Kyunghyun Sung
- Radiological Sciences, University of California, Los Angeles, Los Angeles, 90095, USA
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7
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Nong W, Huang Q, Gao Y. Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging. Front Oncol 2023; 13:1275773. [PMID: 38044995 PMCID: PMC10691548 DOI: 10.3389/fonc.2023.1275773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023] Open
Abstract
Objectives This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa). Methods A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve. Results The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort. Conclusions CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.
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Affiliation(s)
- Wanxian Nong
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qun Huang
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Yong Gao
- Department of Ultrasound, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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8
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Karagoz A, Alis D, Seker ME, Zeybel G, Yergin M, Oksuz I, Karaarslan E. Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study. Insights Imaging 2023; 14:110. [PMID: 37337101 DOI: 10.1186/s13244-023-01439-0] [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: 01/24/2023] [Accepted: 04/17/2023] [Indexed: 06/21/2023] Open
Abstract
OBJECTIVE To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning. METHODS We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa. RESULTS The PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning. CONCLUSIONS The state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets. CLINICAL RELEVANCE STATEMENT A self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice.
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Affiliation(s)
- Ahmet Karagoz
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
- Artificial Intelligence and Information Technologies, Hevi AI Health, Istanbul, Turkey
| | - Deniz Alis
- Artificial Intelligence and Information Technologies, Hevi AI Health, Istanbul, Turkey.
- Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey.
| | - Mustafa Ege Seker
- School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Gokberk Zeybel
- School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Mert Yergin
- Artificial Intelligence and Information Technologies, Hevi AI Health, Istanbul, Turkey
| | - Ilkay Oksuz
- Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Ercan Karaarslan
- Department of Radiology, School of Medicine, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
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9
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Alis D, Kartal MS, Seker ME, Guroz B, Basar Y, Arslan A, Sirolu S, Kurtcan S, Denizoglu N, Tuzun U, Yildirim D, Oksuz I, Karaarslan E. Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study. Eur J Radiol 2023; 165:110924. [PMID: 37354768 DOI: 10.1016/j.ejrad.2023.110924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/15/2023] [Accepted: 06/09/2023] [Indexed: 06/26/2023]
Abstract
BACKGROUND Although systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers. OBJECTIVES To assess the feasibility of deep learning (DL) for the automated assessment of image quality in bi-parametric MRI scans and compare its performance to that of less-experienced readers. METHODS We used bi-parametric prostate MRI scans from the PI-CAI dataset in this study. A 3-point Likert scale, consisting of poor, moderate, and excellent, was utilized for assessing image quality. Three expert readers established the ground-truth labels for the development (500) and testing sets (100). We trained a 3D DL model on the development set using probabilistic prostate masks and an ordinal loss function. Four less-experienced readers scored the testing set for performance comparison. RESULTS The kappa scores between the DL model and the expert consensus for T2W images and ADC maps were 0.42 and 0.61, representing moderate and good levels of agreement. The kappa scores between the less-experienced readers and the expert consensus for T2W images and ADC maps ranged from 0.39 to 0.56 (fair to moderate) and from 0.39 to 0.62 (fair to good). CONCLUSIONS Deep learning (DL) can offer performance comparable to that of less-experienced readers when assessing image quality in bi-parametric prostate MRI, making it a viable option for an automated quality assessment tool. We suggest that DL models trained on more representative datasets, annotated by a larger group of experts, could yield reliable image quality assessment and potentially substitute or assist visual evaluations by human readers.
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Affiliation(s)
- Deniz Alis
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, 34457, Turkey.
| | | | - Mustafa Ege Seker
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Istanbul, 34752, Turkey
| | - Batuhan Guroz
- Acibadem Mehmet Ali Aydinlar University, School of Medicine, Department of Radiology, Istanbul, 34457, Turkey
| | - Yeliz Basar
- Acibadem Healthcare Group, Department of Radiology, Istanbul, 34457, Turkey
| | - Aydan Arslan
- Umraniye Training and Research Hospital, Department of Radiology, Istanbul, 34764, Turkey
| | - Sabri Sirolu
- Istanbul Sisli Hamidiye Etfal Training and Research Hospital, Department of Radiology, Istanbul, 34396, Turkey
| | - Serpil Kurtcan
- Acibadem Healthcare Group, Department of Radiology, Istanbul, 34457, Turkey.
| | - Nurper Denizoglu
- Acibadem Healthcare Group, Department of Radiology, Istanbul, 34457, Turkey.
| | - Umit Tuzun
- Neolife, Radiology Center, Istanbul, 34340, Turkey.
| | - Duzgun Yildirim
- Acibadem Mehmet Ali Aydinlar University, School of Vocational Sciences, Department of Radiology, Istanbul, 34457, Turkey.
| | - Ilkay Oksuz
- Istanbul Technical University, Department of Computer Engineering, Istanbul, 34467, Turkey
| | - Ercan Karaarslan
- Cumhuriyet University, School of Medicine, Sivas, 581407, Turkey.
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10
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Zhong JG, Shi L, Liu J, Cao F, Ma YQ, Zhang Y. Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance. Sci Rep 2023; 13:4846. [PMID: 36964192 PMCID: PMC10038986 DOI: 10.1038/s41598-023-31869-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 03/20/2023] [Indexed: 03/26/2023] Open
Abstract
To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T2-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.
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Affiliation(s)
- Jian-Guo Zhong
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Shi
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jing Liu
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Fang Cao
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yan-Qing Ma
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yang Zhang
- Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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11
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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.
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Affiliation(s)
- Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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12
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A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer. J Clin Med 2022; 12:jcm12010140. [PMID: 36614941 PMCID: PMC9821561 DOI: 10.3390/jcm12010140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/16/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Pooling radiomic features coming from different centers in a statistical framework is challenging due to the variability in scanner models, acquisition protocols, and reconstruction settings. To remove technical variability, commonly called batch effects, different statistical harmonization strategies have been widely used in genomics but less considered in radiomics. The aim of this work was to develop a framework of analysis to facilitate the harmonization of multicenter radiomic features extracted from prostate T2-weighted magnetic resonance imaging (MRI) and to improve the power of radiomics for prostate cancer (PCa) management in order to develop robust non-invasive biomarkers translating into clinical practice. To remove technical variability and correct for batch effects, we investigated four different statistical methods (ComBat, SVA, Arsynseq, and mixed effect). The proposed approaches were evaluated using a dataset of 210 prostate cancer (PCa) patients from two centers. The impacts of the different statistical approaches were evaluated by principal component analysis and classification methods (LogitBoost, random forest, K-nearest neighbors, and decision tree). The ComBat method outperformed all other methods by achieving 70% accuracy and 78% AUC with the random forest method to automatically classify patients affected by PCa. The proposed statistical framework enabled us to define and develop a standardized pipeline of analysis to harmonize multicenter T2W radiomic features, yielding great promise to support PCa clinical practice.
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13
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Wen J, Ji Y, Han J, Shen X, Qiu Y. Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis. Front Oncol 2022; 12:1013941. [PMID: 36248983 PMCID: PMC9554626 DOI: 10.3389/fonc.2022.1013941] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/13/2022] [Indexed: 11/13/2022] Open
Abstract
Objectives We aimed to systematically assess the inter-reader agreement of the Prostate Imaging Reporting and Data System Version (PI-RADS) v2.1 for the detection of prostate cancer (PCa). Methods We included studies reporting inter-reader agreement of different radiologists that applied PI-RADS v2.1 for the detection of PCa. Quality assessment of the included studies was performed with the Guidelines for Reporting Reliability and Agreement Studies. The summary estimates of the inter-reader agreement were pooled with the random-effect model and categorized (from slight to almost perfect) according to the kappa (κ) value. Multiple subgroup analyses and meta-regression were performed to explore various clinical settings. Results A total of 12 studies comprising 2475 patients were included. The pooled inter-reader agreement for whole gland was κ=0.65 (95% CI 0.56-0.73), and for transitional zone (TZ) lesions was κ=0.62 (95% CI 0.51-0.72). There was substantial heterogeneity presented throughout the studies (I 2= 95.6%), and meta-regression analyses revealed that only readers' experience (<5 years vs. ≥5 years) was the significant factor associated with heterogeneity (P<0.01). In studies providing head-to-head comparison, there was no significant difference in inter-reader agreement between PI-RADS v2.1 and v2.0 for both the whole gland (0.64 vs. 0.57, p=0.37), and TZ (0.61 vs. 0.59, p=0.81). Conclusions PI-RADS v2.1 demonstrated substantial inter-reader agreement among radiologists for whole gland and TZ lesions. However, the difference in agreement between PI-RADS v2.0 and v2.1 was not significant for the whole gland or the TZ.
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Affiliation(s)
- Jing Wen
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Yugang Ji
- The First People’s Hospital of Yancheng, The Fourth Affiliated Hospital of Nantong University, Yancheng, China
| | - Jing Han
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, China
| | - Xiaocui Shen
- Department of Medical Imaging, Jiangsu Vocational College of Medicine, Yancheng, China
| | - Yi Qiu
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, China
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14
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Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers. Radiol Med 2022; 127:1245-1253. [PMID: 36114928 PMCID: PMC9587977 DOI: 10.1007/s11547-022-01555-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/30/2022] [Indexed: 11/17/2022]
Abstract
Objective To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. Materials and methods A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. Results Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). Conclusions AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
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15
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Belue MJ, Yilmaz EC, Daryanani A, Turkbey B. Current Status of Biparametric MRI in Prostate Cancer Diagnosis: Literature Analysis. Life (Basel) 2022; 12:804. [PMID: 35743835 PMCID: PMC9224842 DOI: 10.3390/life12060804] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/23/2022] [Accepted: 05/23/2022] [Indexed: 12/19/2022] Open
Abstract
The role of multiparametric MRI (mpMRI) in the detection of prostate cancer is well-established. Based on the limited role of dynamic contrast enhancement (DCE) in PI-RADS v2.1, the risk of potential side effects, and the increased cost and time, there has been an increase in studies advocating for the omission of DCE from MRI assessments. Per PI-RADS v2.1, DCE is indicated in the assessment of PI-RADS 3 lesions in the peripheral zone, with its most pronounced effect when T2WI and DWI are of insufficient quality. The aim of this study was to evaluate the methodology and reporting in the literature from the past 5 years regarding the use of DCE in prostate MRI, especially with respect to the indications for DCE as stated in PI-RADS v2.1, and to describe the different approaches used across the studies. We searched for studies investigating the use of bpMRI and/or mpMRI in the detection of clinically significant prostate cancer between January 2017 and April 2022 in the PubMed, Web of Science, and Google Scholar databases. Through the search process, a total of 269 studies were gathered and 41 remained after abstract and full-text screening. The following information was extracted from the eligible studies: general clinical and technical characteristics of the studies, the number of PI-RADS 3 lesions, different definitions of clinically significant prostate cancer (csPCa), biopsy thresholds, reference standard methods, and number and experience of readers. Forty-one studies were included in the study. Only 51% (21/41) of studies reported the prevalence of csPCa in their equivocal lesion (PI-RADS category 3 lesions) subgroups. Of the included studies, none (0/41) performed a stratified sub-analysis of the DCE benefit versus MRI quality and 46% (19/41) made explicit statements about removing MRI scans based on a range of factors including motion, noise, and image artifacts. Furthermore, the number of studies investigating the role of DCE using readers with varying experience was relatively low. This review demonstrates that a high proportion of the studies investigating whether bpMRI can replace mpMRI did not transparently report information inherent to their study design concerning the key indications of DCE, such as the number of clinically insignificant/significant PI-RADS 3 lesions, nor did they provide any sub-analyses to test image quality, with some removing bad quality MRI scans altogether, or reader-experience-dependency indications for DCE. For the studies that reported on most of the DCE indications, their conclusions about the utility of DCE were heavily definition-dependent (with varying definitions of csPCa and of the PI-RADS category biopsy significance threshold). Reporting the information inherent to the study design and related to the specific indications for DCE as stated in PI-RADS v2.1 is needed to determine whether DCE is helpful or not. With most of the recent literature being retrospective and not including the data related to DCE indications in particular, the ongoing dispute between bpMRI and mpMRI is likely to linger.
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Affiliation(s)
| | | | | | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892-9760, USA; (M.J.B.); (E.C.Y.); (A.D.)
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16
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Mata C, Walker P, Oliver A, Martí J, Lalande A. Usefulness of Collaborative Work in the Evaluation of Prostate Cancer from MRI. Clin Pract 2022; 12:350-362. [PMID: 35645317 PMCID: PMC9149964 DOI: 10.3390/clinpract12030040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland (central and peripheral zones as well as tumour) by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer. One of the experts then drew the ROI with knowledge of the other expert’s ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI.
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Affiliation(s)
- Christian Mata
- Pediatric Computational Imaging Research Group, Hospital Sant Joan de Déu, 08950 Esplugues de Llobregat, Spain
- Research Centre for Biomedical Engineering (CREB), Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
- Correspondence:
| | - Paul Walker
- ImViA Laboratory, Université de Bourgogne Franche-Comté, 64 Rue de Sully, 21000 Dijon, France; (P.W.); (A.L.)
| | - Arnau Oliver
- Institute of Computer Vision and Robotics, University of Girona, Campus Montilivi, Ed. P-IV, 17003 Girona, Spain; (A.O.); (J.M.)
| | - Joan Martí
- Institute of Computer Vision and Robotics, University of Girona, Campus Montilivi, Ed. P-IV, 17003 Girona, Spain; (A.O.); (J.M.)
| | - Alain Lalande
- ImViA Laboratory, Université de Bourgogne Franche-Comté, 64 Rue de Sully, 21000 Dijon, France; (P.W.); (A.L.)
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17
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:2599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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18
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Diagnostic value of integrated 18F-PSMA-1007 PET/MRI Compared with that of Biparametric MRI for the detection of Prostate Cancer. Prostate Int 2022; 10:108-116. [PMID: 35510079 PMCID: PMC9052074 DOI: 10.1016/j.prnil.2022.03.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 03/09/2022] [Accepted: 03/22/2022] [Indexed: 12/24/2022] Open
Abstract
Objective Materials and methods Results Conclusion
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19
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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: 76] [Impact Index Per Article: 25.3] [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.
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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.
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20
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Razek AAKA, El-Diasty T, Elhendy A, Fahmy D, El-Adalany MA. Prostate Imaging Reporting and Data System (PI-RADS): What the radiologists need to know? Clin Imaging 2021; 79:183-200. [PMID: 34098371 DOI: 10.1016/j.clinimag.2021.05.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 01/14/2023]
Abstract
We aim to review the new modifications in MR imaging technique, image interpretation, lexicon, and scoring system of the last version of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) in a simple and practical way. This last version of PI-RADS v2.1 describes the new technical modifications in the protocol of Multiparametric MRI (MpMRI) including T2, diffusion-weighted imaging (DWI), and dynamic contrast enhancement (DCE) parameters. It includes also; new guidelines in the image interpretation specifications in new locations (lesions located in the central zone and anterior fibromuscular stroma), clarification of T2 scoring of lesions of the transition zone, the distinction between DWI score 2 and 3 lesions in the transition zone and peripheral zone, as well as between positive and negative enhancement in DCE. Biparametric MRI (BpMRI) along with simplified PI-RADS is gaining more acceptances in the assessment of clinically significant prostatic cancer.
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Affiliation(s)
| | - Tarek El-Diasty
- Department of Diagnostic Radiology, Mansoura Urology and Nephrology Center, Mansoura, Egypt
| | - Ahmed Elhendy
- Department of Diagnostic Radiology, Mansoura Urology and Nephrology Center, Mansoura, Egypt
| | - Dalia Fahmy
- Department of Diagnostic Radiology, Mansoura Faculty of Medicine, Mansoura, Egypt
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21
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Park KJ, Choi SH, Kim M, Kim JK, Jeong IG. Performance of Prostate Imaging Reporting and Data System Version 2.1 for Diagnosis of Prostate Cancer: A Systematic Review and
Meta‐Analysis. J Magn Reson Imaging 2021; 54:103-112. [DOI: 10.1002/jmri.27546] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 02/01/2023] Open
Affiliation(s)
- Kye Jin Park
- Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea
| | - Sang Hyun Choi
- Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea
| | - Mi‐hyun Kim
- Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea
| | - Jeong Kon Kim
- Department of Radiology and Research Institute of Radiology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea
| | - In Gab Jeong
- Department of Urology University of Ulsan College of Medicine, Asan Medical Center Seoul Republic of Korea
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22
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Brancato V, Aiello M, Basso L, Monti S, Palumbo L, Di Costanzo G, Salvatore M, Ragozzino A, Cavaliere C. Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions. Sci Rep 2021; 11:643. [PMID: 33436929 PMCID: PMC7804929 DOI: 10.1038/s41598-020-80749-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 12/24/2020] [Indexed: 12/11/2022] Open
Abstract
Despite the key-role of the Prostate Imaging and Reporting and Data System (PI-RADS) in the diagnosis and characterization of prostate cancer (PCa), this system remains to be affected by several limitations, primarily associated with the interpretation of equivocal PI-RADS 3 lesions and with the debated role of Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), which is only used to upgrade peripheral PI-RADS category 3 lesions to PI-RADS category 4 if enhancement is focal. We aimed at investigating the usefulness of radiomics for detection of PCa lesions (Gleason Score ≥ 6) in PI-RADS 3 lesions and in peripheral PI-RADS 3 upgraded to PI-RADS 4 lesions (upPI-RADS 4). Multiparametric MRI (mpMRI) data of patients who underwent prostatic mpMRI between April 2013 and September 2018 were retrospectively evaluated. Biopsy results were used as gold standard. PI-RADS 3 and PI-RADS 4 lesions were re-scored according to the PI-RADS v2.1 before and after DCE-MRI evaluation. Radiomic features were extracted from T2-weighted MRI (T2), Apparent diffusion Coefficient (ADC) map and DCE-MRI subtracted images using PyRadiomics. Feature selection was performed using Wilcoxon-ranksum test and Minimum Redundancy Maximum Relevance (mRMR). Predictive models were constructed for PCa detection in PI-RADS 3 and upPI-RADS 4 lesions using at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. 41 PI-RADS 3 and 32 upPI-RADS 4 lesions were analyzed. Among 293 radiomic features, the top selected features derived from T2 and ADC. For PI-RADS 3 stratification, second order model showed higher performances (Area Under the Receiver Operating Characteristic Curve-AUC- = 80%), while for upPI-RADS 4 stratification, first order model showed higher performances respect to superior order models (AUC = 89%). Our results support the significant role of T2 and ADC radiomic features for PCa detection in lesions scored as PI-RADS 3 and upPI-RADS 4. Radiomics models showed high diagnostic efficacy in classify PI-RADS 3 and upPI-RADS 4 lesions, outperforming PI-RADS v2.1 performance.
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Affiliation(s)
| | | | | | - Serena Monti
- Institute of Biostructures and Bioimaging, National Research Council, Naples, Italy
| | - Luigi Palumbo
- Department of Radiology, S. Maria Delle Grazie Hospital, Pozzuoli, Italy
| | | | | | - Alfonso Ragozzino
- Department of Radiology, S. Maria Delle Grazie Hospital, Pozzuoli, Italy
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23
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Palumbo P, Manetta R, Izzo A, Bruno F, Arrigoni F, De Filippo M, Splendiani A, Di Cesare E, Masciocchi C, Barile A. Biparametric (bp) and multiparametric (mp) magnetic resonance imaging (MRI) approach to prostate cancer disease: a narrative review of current debate on dynamic contrast enhancement. Gland Surg 2020; 9:2235-2247. [PMID: 33447576 DOI: 10.21037/gs-20-547] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Prostate cancer is the most common malignancy in male population. Over the last few years, magnetic resonance imaging (MRI) has proved to be a robust clinical tool for identification and staging of clinically significant prostate cancer. Though suggestions by the European Society of Urogenital Radiology to use complete multiparametric (mp) T2-weighted/diffusion weighted imaging (DWI)/dynamic contrast enhancement (DCE) acquisition for all prostate MRI examinations, the real advantage of functional DCE remains a matter of debate. Recent studies demonstrate that biparametric (bp) and mp approaches have similar accuracy, but controversial evidences remain, and the specific potential benefits of contrast medium administration are still poorly discussed in literature. The bp approach is in fact sufficient in most cases to adequately identify a negative test, or to accurately define the degree of aggressiveness of a lesion, especially if larger or with major characteristics of malignancy. This feature would give the DCE a secondary role, probably limited to a second evaluation of the lesion location, for detecting small cancer or in case of controversy. However, DCE has proved to increase the sensitivity of prostate MRI, though a less specificity. Therefore, an appropriate decision algorithm is needed to standardize the MRI approach. Aim of this review study was to provide a schematic description of bpMRI and mpMRI approaches in the study of prostatic anatomy, focusing on comparative validity and current DCE application. Additional theoretical considerations on prostate MRI are provided.
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Affiliation(s)
- Pierpaolo Palumbo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Rosa Manetta
- Radiology Unit, San Salvatore Hospital, L'Aquila, Italy
| | - Antonio Izzo
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Federico Bruno
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Francesco Arrigoni
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Massimo De Filippo
- Department of Medicine and Surgery (DiMec), Section of Radiology, University of Parma, Maggiore Hospital, Parma, Italy
| | - Alessandra Splendiani
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Carlo Masciocchi
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Antonio Barile
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
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24
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PI-RADS Version 2.1: A Critical Review, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol 2020; 216:20-32. [PMID: 32997518 DOI: 10.2214/ajr.20.24495] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PI-RADS version 2.1 updates the technical parameters for multiparametric MRI (mpMRI) of the prostate and revises the imaging interpretation criteria while maintaining the framework introduced in version 2. These changes have been considered an improvement, although some issues remain unresolved, and new issues have emerged. Areas for improvement discussed in this review include the need for more detailed mpMRI protocols with optimization for 1.5-T and 3-T systems; lack of validation of revised transition zone interpretation criteria and need for clarifications of the revised DWI and dynamic contrast-enhanced imaging criteria and central zone (CZ) assessment; the need for systematic evaluation and reporting of background changes in signal intensity in the prostate that can negatively affect cancer detection; creation of a new category for lesions that do not fit into the PI-RADS assessment categories (i.e., PI-RADS M category); inclusion of quantitative parameters beyond size to evaluate lesion aggressiveness; adjustments to the structured report template, including standardized assessment of the risk of extraprostatic extension; development of parameters for image quality and performance control; and suggestions for expansion of the system to other indications (e.g., active surveillance and recurrence).
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25
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Walker SM, Türkbey B. PI-RADSv2.1: Current status. Turk J Urol 2020; 47:S45-S48. [PMID: 33052842 DOI: 10.5152/tud.2020.20403] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 09/03/2020] [Indexed: 12/24/2022]
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has played an increasing role in the detection and local staging of prostate cancer over the last 15 years. Prostate mpMRI, due to various factors, is prone to high inter-reader variability necessitating standardized reporting guidelines that provide accurate and actionable information to the ordering clinician. The Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) was released in March 2019 as an update to PI-RADSv2.0 with the hope of further standardizing the reporting process of prostate mpMRI, improving the detection of clinically significant cancer, reducing the biopsy rate of indolent tumors, and decreasing inter-reader variability. Early data show an improved performance of PI-RADSv2.1 over PI-RADSv2.0. Updates included in PI-RADSv2.1 and its current experience in clinic will be reviewed in this review.
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Affiliation(s)
- Stephanie M Walker
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Barış Türkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
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26
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A Prostate MRI Segmentation Tool Based on Active Contour Models Using a Gradient Vector Flow. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Medical support systems used to assist in the diagnosis of prostate lesions generally related to prostate segmentation is one of the majors focus of interest in recent literature. The main problem encountered in the diagnosis of a prostate study is the localization of a Regions of Interest (ROI) containing a tumor tissue. In this paper, a new GUI tool based on a semi-automatic prostate segmentation is presented. The main rationale behind this tool and the focus of this article is facilitate the time consuming segmentation process used for annotating images in the clinical practice, enabling the radiologists to use novel and easy to use semi-automatic segmentation techniques instead of manual segmentation. In this work, a detailed specification of the proposed segmentation algorithm using an Active Contour Models (ACM) aided with a Gradient Vector Flow (GVF) component is defined. The purpose is to help the manual segmentation process of the main ROIs of prostate gland zones. Finally, an experimental case of use and a discussion part of the results are presented.
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