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Ren S, Li J, Dorado J, Sierra A, González-Díaz H, Duardo A, Shen B. From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine. Health Inf Sci Syst 2024; 12:6. [PMID: 38125666 PMCID: PMC10728428 DOI: 10.1007/s13755-023-00264-5] [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: 08/24/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.
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Affiliation(s)
- Shumin Ren
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Jiakun Li
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
| | - Julián Dorado
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
| | - Alejandro Sierra
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Humbert González-Díaz
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Aliuska Duardo
- Department of Computer Science and Information Technology, University of A Coruña, 15071 A Coruña, Spain
- IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
| | - Bairong Shen
- Department of Urology and Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041 China
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Teodorescu B, Gilberg L, Melton PW, Hehr RM, Guzel HE, Koc AM, Baumgart A, Maerkisch L, Ataide EJG. A systematic review of deep learning-based spinal bone lesion detection in medical images. Acta Radiol 2024:2841851241263066. [PMID: 39033391 DOI: 10.1177/02841851241263066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
Spinal bone lesions encompass a wide array of pathologies, spanning from benign abnormalities to aggressive malignancies, such as diffusely localized metastases. Early detection and accurate differentiation of the underlying diseases is crucial for every patient's clinical treatment and outcome, with radiological imaging being a core element in the diagnostic pathway. Across numerous pathologies and imaging techniques, deep learning (DL) models are progressively considered a valuable resource in the clinical setting. This review describes not only the diagnostic performance of these models and the differing approaches in the field of spinal bone malignancy recognition, but also the lack of standardized methodology and reporting that we believe is currently hampering this newly founded area of research. In line with their established and reliable role in lesion detection, this publication focuses on both computed tomography and magnetic resonance imaging, as well as various derivative modalities (i.e. SPECT). After conducting a systematic literature search and subsequent analysis for applicability and quality using a modified QUADAS-2 scoring system, we confirmed that most of the 14 identified studies were plagued by major limitations, such as insufficient reporting of model statistics and data acquisition, a lacking external validation dataset, and potentially biased annotation. Although we experienced these limitations, we nonetheless conclude that the potential of these methods shines through in the presented results. These findings underline the need for more stringent quality controls in DL studies, as well as model development to afford increased insight and progress in this promising novel field.
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Affiliation(s)
- Bianca Teodorescu
- Floy GmbH, Munich, Germany
- Department of Medicine II, University Hospital, LMU Munich, Munich, Germany
| | - Leonard Gilberg
- Floy GmbH, Munich, Germany
- Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany
| | - Philip William Melton
- Floy GmbH, Munich, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Munich, Germany
| | | | - Hamza Eren Guzel
- Floy GmbH, Munich, Germany
- University of Health Sciences İzmir Bozyaka Research and Training Hospital, Izmir, Turkey
| | - Ali Murat Koc
- Floy GmbH, Munich, Germany
- Ataturk Education and Research Hospital, Department of Radiology, Izmir Katip Celebi University, Izmir, Turkey
| | - Andre Baumgart
- Mannheim Institute of Public Health, Universität Medizin Mannheim, Mannheim, Germany
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Reinhardt C, Briody H, MacMahon PJ. AI-accelerated prostate MRI: a systematic review. Br J Radiol 2024; 97:1234-1242. [PMID: 38718224 PMCID: PMC11186563 DOI: 10.1093/bjr/tqae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 04/05/2024] [Accepted: 05/04/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges. METHODS A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements. RESULTS Two primary findings were observed. Firstly, all studies indicated that AI-acceleration in MRI achieved notable reductions in acquisition times without compromising image quality. This efficiency offers potential clinical advantages, including reduced scan durations, improved scheduling, diminished patient discomfort, and economic benefits. Secondly, AI demonstrated a beneficial effect in reducing or maintaining artefact levels in T2-weighted images despite this accelerated acquisition time. Inconsistent results were found in all other domains, which were likely influenced by factors such as heterogeneity in methodologies, variability in AI models, and diverse radiologist profiles. These variances underscore the need for larger, more robust studies, standardization, and diverse training datasets for AI models. CONCLUSION The integration of AI-acceleration in prostate MRI thus far shows some promising results for efficient and enhanced scanning. These advancements may fill current gaps in early detection and prognosis. However, careful navigation and collaborative efforts are essential to overcome challenges and maximize the potential of this innovative and evolving field. ADVANCES IN KNOWLEDGE This article reveals overall significant reductions in acquisition time without compromised image quality in AI-accelerated prostate MRI, highlighting potential clinical and diagnostic advantages.
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Affiliation(s)
- Ciaran Reinhardt
- Department of Radiology, Mater Misericordiae Hospital, Dublin D07AX57, Ireland
| | - Hayley Briody
- Department of Radiology, Beaumont Hospital, Dublin, D09V2N0, Ireland
| | - Peter J MacMahon
- Department of Radiology, Mater Misericordiae Hospital, Dublin D07AX57, Ireland
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Russo L, Bottazzi S, Sala E. Artificial intelligence in female pelvic oncology: tailoring applications to clinical needs. Eur Radiol 2024; 34:4038-4040. [PMID: 37989917 DOI: 10.1007/s00330-023-10455-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/23/2023]
Affiliation(s)
- Luca Russo
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.
- Università Cattolica del Sacro Cuore, Rome, Italy.
| | | | - Evis Sala
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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5
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Yang C, Li B, Luan Y, Wang S, Bian Y, Zhang J, Wang Z, Liu B, Chen X, Hacker M, Li Z, Li X, Wang Z. Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score. Urol Oncol 2024; 42:158.e17-158.e27. [PMID: 38388243 DOI: 10.1016/j.urolonc.2024.01.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/31/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds great potential for automating csPCa classification on mpMRI. METHOD To compare the performance between a DL algorithm and PI-RADS categorization in PCa detection and csPCa classification, we included 1,729 consecutive patients who underwent radical prostatectomy or biopsy in Tongji hospital. We developed DL models by integrating individual mpMRI sequences and employing an ensemble approach for distinguishing between csPCa and CiSPCa (specifically defined as PCa with a Gleason group 1 or benign prostate disease, training cohort: 1,285 patients vs. external testing cohort: 315 patients). RESULTS DL-based models exhibited higher csPCa detection rates than PI-RADS categorization (area under the curve [AUC]: 0.902; sensitivity: 0.728; specificity: 0.906 vs. AUC: 0.759; sensitivity: 0.761; specificity: 0.756) (P < 0.001) Notably, DL networks exhibited significant strength in the prostate-specific antigen (PSA) arm < 10 ng/ml compared with PI-RADS assessment (AUC: 0.788; sensitivity: 0.588; specificity: 0.883 vs. AUC: 0.618; sensitivity: 0.379; specificity: 0.763) (P = 0.041). CONCLUSIONS We developed DL-based mpMRI ensemble models for csPCa classification with improved sensitivity, specificity, and accuracy compared with clinical PI-RADS assessment. In the PSA-stratified condition, the DL ensemble model performed better than PI-RADS in the detection of csPCa in both the high PSA group and the low PSA group.
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Affiliation(s)
- Chunguang Yang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Basen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Luan
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiwei Wang
- Evomics Medical Technology Co., Ltd., Shanghai, China
| | - Yang Bian
- Evomics Medical Technology Co., Ltd., Shanghai, China
| | - Junbiao Zhang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zefeng Wang
- Department of Urology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Bo Liu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xin Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Zhen Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiang Li
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Department of nuclear medicine, Beijing Chest Hospital, Capital Medical University, Beijing, China.
| | - Zhihua Wang
- Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Kessler DA, Graves MJ. Editorial for "The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI". J Magn Reson Imaging 2024; 59:1807-1808. [PMID: 37534877 DOI: 10.1002/jmri.28939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023] Open
Affiliation(s)
| | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge, UK
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Lang J, McClure TD, Margolis DJA. MRI-Ultrasound Fused Approach for Prostate Biopsy-How It Is Performed. Cancers (Basel) 2024; 16:1424. [PMID: 38611102 PMCID: PMC11010881 DOI: 10.3390/cancers16071424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/02/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024] Open
Abstract
The use of MRI-ultrasound image fusion targeted biopsy of the prostate in the face of an elevated serum PSA is now recommended by multiple societies, and results in improved detection of clinically significant cancer and, potentially, decreased detection of indolent disease. This combines the excellent sensitivity of MRI for clinically significant prostate cancer and the real-time biopsy guidance and confirmation of ultrasound. Both transperineal and transrectal approaches can be implemented using cognitive fusion, mechanical fusion with an articulated arm and electromagnetic registration, or pure software registration. The performance has been shown comparable to in-bore MRI biopsy performance. However, a number of factors influence the performance of this technique, including the quality and interpretation of the MRI, the approach used for biopsy, and experience of the practitioner, with most studies showing comparable performance of MRI-ultrasound fusion to in-bore targeted biopsy. Future improvements including artificial intelligence promise to refine the performance of all approaches.
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Affiliation(s)
- Jacob Lang
- Department of Urology, Weill Cornell Medicine, New York, NY 10068, USA
| | - Timothy Dale McClure
- Department of Urology, Weill Cornell Medicine, New York, NY 10068, USA
- Department of Radiology, Weill Cornell Medicine, New York, NY 10068, USA
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Sushentsev N, Barrett T, Rundo L. Reply to a Letter to the Editor on Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review. Insights Imaging 2024; 15:43. [PMID: 38353766 PMCID: PMC10866832 DOI: 10.1186/s13244-023-01594-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 02/17/2024] Open
Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
| | - Leonardo Rundo
- Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Fisciano, SA, Italy
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9
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Gelikman DG, Rais-Bahrami S, Pinto PA, Turkbey B. AI-powered radiomics: revolutionizing detection of urologic malignancies. Curr Opin Urol 2024; 34:1-7. [PMID: 37909882 PMCID: PMC10842165 DOI: 10.1097/mou.0000000000001144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
PURPOSE OF REVIEW This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. RECENT FINDINGS As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. SUMMARY Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.
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Affiliation(s)
- David G Gelikman
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Soroush Rais-Bahrami
- Department of Urology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
- O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
- Department of Radiology, The University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Baris Turkbey
- Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Arber T, Jaouen T, Campoy S, Rabilloud M, Souchon R, Abbas F, Moldovan PC, Colombel M, Crouzet S, Ruffion A, Neuville P, Rouvière O. Zone-specific computer-aided diagnosis system aimed at characterizing ISUP ≥ 2 prostate cancers on multiparametric magnetic resonance images: evaluation in a cohort of patients on active surveillance. World J Urol 2023; 41:3527-3533. [PMID: 37845554 DOI: 10.1007/s00345-023-04643-1] [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: 07/30/2023] [Accepted: 09/15/2023] [Indexed: 10/18/2023] Open
Abstract
PURPOSE To assess a region-of-interest-based computer-assisted diagnosis system (CAD) in characterizing aggressive prostate cancer on magnetic resonance imaging (MRI) from patients under active surveillance (AS). METHODS A prospective biopsy database was retrospectively searched for patients under AS who underwent MRI and subsequent biopsy at our institution. MRI lesions targeted at baseline biopsy were retrospectively delineated to calculate the CAD score that was compared to the Prostate Imaging-Reporting and Data System (PI-RADS) version 2 score assigned at baseline biopsy. RESULTS 186 patients were selected. At baseline biopsy, 51 and 15 patients had International Society of Urological Pathology (ISUP) grade ≥ 2 and ≥ 3 cancer respectively. The CAD score had significantly higher specificity for ISUP ≥ 2 cancers (60% [95% confidence interval (CI): 51-68]) than the PI-RADS score (≥ 3 dichotomization: 24% [CI: 17-33], p = 0.0003; ≥ 4 dichotomization: 32% [CI: 24-40], p = 0.0003). It had significantly lower sensitivity than the PI-RADS ≥ 3 dichotomization (85% [CI: 74-92] versus 98% [CI: 91-100], p = 0.015) but not than the PI-RADS ≥ 4 dichotomization (94% [CI:85-98], p = 0.104). Combining CAD findings and PSA density could have avoided 47/184 (26%) baseline biopsies, while missing 3/51 (6%) ISUP 2 and no ISUP ≥ 3 cancers. Patients with baseline negative CAD findings and PSAd < 0.15 ng/mL2 who stayed on AS after baseline biopsy had a 9% (4/44) risk of being diagnosed with ISUP ≥ 2 cancer during a median follow-up of 41 months, as opposed to 24% (18/74) for the others. CONCLUSION The CAD could help define AS patients with low risk of aggressive cancer at baseline assessment and during subsequent follow-up.
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Affiliation(s)
- Théo Arber
- Department of Urology, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
| | | | - Séphora Campoy
- 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
| | - Muriel Rabilloud
- 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
- Université de Lyon, Lyon, France
- Université Lyon 1, Lyon, France
| | | | - Fatima Abbas
- 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
| | - Paul C Moldovan
- Department of Radiology, Hospices Civils de Lyon, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Marc Colombel
- Université de Lyon, Lyon, France
- Université Lyon 1, Lyon, France
- Department of Urology, Hospices Civils de Lyon, Hôpital Edouard Herriot, 69437, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
| | - Sébastien Crouzet
- LabTau, INSERM U1032, Lyon, France
- Université de Lyon, Lyon, France
- Université Lyon 1, Lyon, France
- Department of Urology, Hospices Civils de Lyon, Hôpital Edouard Herriot, 69437, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
| | - Alain Ruffion
- Department of Urology, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
- Université de Lyon, Lyon, France
- Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Sud, Pierre Bénite, France
| | - Paul Neuville
- Department of Urology, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
| | - Olivier Rouvière
- LabTau, INSERM U1032, Lyon, France.
- Université de Lyon, Lyon, France.
- Université Lyon 1, Lyon, France.
- Department of Radiology, Hospices Civils de Lyon, Hôpital Edouard Herriot, 69437, Lyon, France.
- Faculté de Médecine Lyon Est, Lyon, France.
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11
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Bevilacqua A, Mottola M. Letter to the Editor on "Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review". Insights Imaging 2023; 14:183. [PMID: 37923836 PMCID: PMC10624779 DOI: 10.1186/s13244-023-01520-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 08/23/2023] [Indexed: 11/06/2023] Open
Affiliation(s)
- Alessandro Bevilacqua
- Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy.
- Advanced Research Center On Electronics Systems (ARCES), University of Bologna, Bologna, Italy.
| | - Margherita Mottola
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
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12
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Jaouen T, Souchon R, Moldovan PC, Bratan F, Duran A, Hoang-Dinh A, Di Franco F, Debeer S, Dubreuil-Chambardel M, Arfi N, Ruffion A, Colombel M, Crouzet S, Gonindard-Melodelima C, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader. Diagn Interv Imaging 2023; 104:465-476. [PMID: 37345961 DOI: 10.1016/j.diii.2023.04.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 04/16/2023] [Accepted: 04/18/2023] [Indexed: 06/23/2023]
Abstract
PURPOSE The purpose of this study was to develop and test across various scanners a zone-specific region-of-interest (ROI)-based computer-aided diagnosis system (CAD) aimed at characterizing, on MRI, International Society of Urological Pathology (ISUP) grade≥2 prostate cancers. MATERIALS AND METHODS ROI-based quantitative models were selected in multi-vendor training (265 pre-prostatectomy MRIs) and pre-test (112 pre-biopsy MRIs) datasets. The best peripheral and transition zone models were combined and retrospectively assessed in internal (158 pre-biopsy MRIs) and external (104 pre-biopsy MRIs) test datasets. Two radiologists (R1/R2) retrospectively delineated the lesions targeted at biopsy in test datasets. The CAD area under the receiver operating characteristic curve (AUC) for characterizing ISUP≥2 cancers was compared to that of the Prostate Imaging-Reporting and Data System version2 (PI-RADSv2) score prospectively assigned to targeted lesions. RESULTS The best models used the 25th apparent diffusion coefficient (ADC) percentile in transition zone and the 2nd ADC percentile and normalized wash-in rate in peripheral zone. The PI-RADSv2 AUCs were 82% (95% confidence interval [CI]: 74-87) and 86% (95% CI: 81-91) in the internal and external test datasets respectively. They were not different from the CAD AUCs obtained with R1 and R2 delineations, in the internal (82% [95% CI: 76-89], P = 0.95 and 85% [95% CI: 78-91], P = 0.55) and external (82% [95% CI: 74-91], P = 0.41 and 86% [95% CI:78-95], P = 0.98) test datasets. The CAD yielded sensitivities of 86-89% and 90-91%, and specificities of 64-65% and 69-75% in the internal and external test datasets respectively. CONCLUSION The CAD performance for characterizing ISUP grade≥2 prostate cancers on MRI is not different from that of PI-RADSv2 score across two test datasets.
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Affiliation(s)
| | | | - Paul C Moldovan
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Flavie Bratan
- Hôpital Saint Joseph Saint Luc, Department of Radiology, Lyon, 69007, France
| | - Audrey Duran
- Univ Lyon, CNRS, Inserm, INSA Lyon, UCBL, CREATIS, UMR5220, U1294, Villeurbanne, 69100, France
| | - Au Hoang-Dinh
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hanoi Medical University, Department of Radiology, Hanoi, 116001, Vietnam
| | - Florian Di Franco
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Sabine Debeer
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Marine Dubreuil-Chambardel
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France
| | - Nicolas Arfi
- Hôpital Saint Joseph Saint Luc, Department of Urology, Lyon, 69007, France
| | - Alain Ruffion
- Hospices Civils de Lyon, Centre Hospitalier Lyon Sud, Department of Urology, Pierre-Bénite, 69310, France; Equipe 2 - Centre d'Innovation en Cancérologie de Lyon (EA 3738 CICLY), Pierre-Bénite, 69310, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Sud, Pierre-Bénite, 69310, France
| | - Marc Colombel
- Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Sébastien Crouzet
- INSERM, LabTAU, U1032, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France
| | - Christelle Gonindard-Melodelima
- Université Grenoble Alpes, Laboratoire d'Ecologie Alpine, BP 53, Grenoble 38041, France; CNRS, UMR 5553, BP 53, Grenoble, 38041, France
| | - Olivier Rouvière
- INSERM, LabTAU, U1032, Lyon, 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon, 69003, France; Université de Lyon, Lyon, 69003, France; Université Lyon 1, Lyon, 69003, France; Faculté de Médecine Lyon Est, Lyon, 69003, France.
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Dominguez I, Rios-Ibacache O, Caprile P, Gonzalez J, San Francisco IF, Besa C. MRI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic Features. Diagnostics (Basel) 2023; 13:2779. [PMID: 37685317 PMCID: PMC10486695 DOI: 10.3390/diagnostics13172779] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
This study aimed to develop a noninvasive Machine Learning (ML) model to identify clinically significant prostate cancer (csPCa) according to Gleason Score (GS) based on biparametric MRI (bpMRI) radiomic features and clinical information. METHODS This retrospective study included 86 adult Hispanic men (60 ± 8.2 years, median prostate-specific antigen density (PSA-D) 0.15 ng/mL2) with PCa who underwent prebiopsy 3T MRI followed by targeted MRI-ultrasound fusion and systematic biopsy. Two observers performed 2D segmentation of lesions in T2WI/ADC images. We classified csPCa (GS ≥ 7) vs. non-csPCa (GS = 6). Univariate statistical tests were performed for different parameters, including prostate volume (PV), PSA-D, PI-RADS, and radiomic features. Multivariate models were built using the automatic feature selection algorithm Recursive Feature Elimination (RFE) and different classifiers. A stratified split separated the train/test (80%) and validation (20%) sets. RESULTS Radiomic features derived from T2WI/ADC are associated with GS in patients with PCa. The best model found was multivariate, including image (T2WI/ADC) and clinical (PV and PSA-D) information. The validation area under the curve (AUC) was 0.80 for differentiating csPCa from non-csPCa, exhibiting better performance than PI-RADS (AUC: 0.71) and PSA-D (AUC: 0.78). CONCLUSION Our multivariate ML model outperforms PI-RADS v2.1 and established clinical indicators like PSA-D in classifying csPCa accurately. This underscores MRI-derived radiomics' (T2WI/ADC) potential as a robust biomarker for assessing PCa aggressiveness in Hispanic patients.
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Affiliation(s)
- Ignacio Dominguez
- Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile;
| | - Odette Rios-Ibacache
- Institute of Physics, Pontifical Catholic University of Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile;
- Medical Physics Unit, McGill University, Montreal, QC H4A 3J1, Canada
| | - Paola Caprile
- Institute of Physics, Pontifical Catholic University of Chile, Av. Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile;
- Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, ANID, Macul, Santiago 7820436, Chile
| | - Jose Gonzalez
- School of Medicine, Pontifical Catholic University of Chile, Santiago 8320000, Chile
| | - Ignacio F. San Francisco
- Department of Urology, School of Medicine, Pontifical Catholic University of Chile, Santiago 8320000, Chile
| | - Cecilia Besa
- Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile;
- Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, ANID, Macul, Santiago 7820436, Chile
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14
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Thomas M, Murali S, Simpson BSS, Freeman A, Kirkham A, Kelly D, Whitaker HC, Zhao Y, Emberton M, Norris JM. Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis. BMJ Open 2023; 13:e074009. [PMID: 37607794 PMCID: PMC10445392 DOI: 10.1136/bmjopen-2023-074009] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/28/2023] [Indexed: 08/24/2023] Open
Abstract
INTRODUCTION Multiparametric MRI (mpMRI) has transformed the prostate cancer diagnostic pathway, allowing for improved risk stratification and more targeted subsequent management. However, concerns exist over the interobserver variability of images and the applicability of this model long term, especially considering the current shortage of radiologists and the growing ageing population. Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. The following report details a protocol for a systematic review and meta-analysis investigating the accuracy of AI in predicting primary prostate cancer on mpMRI. METHODS AND ANALYSIS A systematic search will be performed using PubMed, MEDLINE, Embase and Cochrane databases. All relevant articles published between January 2016 and February 2023 will be eligible for inclusion. To be included, articles must use AI to study MRI prostate images to detect prostate cancer. All included articles will be in full-text, reporting original data and written in English. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The QUADAS-2 score will assess the quality and risk of bias across selected studies. ETHICS AND DISSEMINATION Ethical approval will not be required for this systematic review. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences. PROSPERO REGISTRATION NUMBER CRD42021293745.
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Affiliation(s)
- Maya Thomas
- School of Medicine, Imperial College London, London, UK
| | | | | | - Alex Freeman
- Department of Pathology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Alex Kirkham
- Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Daniel Kelly
- School of Healthcare Sciences, Cardiff University, Cardiff, UK
| | - Hayley C Whitaker
- UCL Division of Surgery and Interventional Science, University College London, London, UK
| | - Yi Zhao
- School of Medicine, Imperial College London, London, UK
| | - Mark Emberton
- UCL Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
| | - Joseph M Norris
- UCL Division of Surgery and Interventional Science, University College London, London, UK
- Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK
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15
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Nematollahi H, Moslehi M, Aminolroayaei F, Maleki M, Shahbazi-Gahrouei D. Diagnostic Performance Evaluation of Multiparametric Magnetic Resonance Imaging in the Detection of Prostate Cancer with Supervised Machine Learning Methods. Diagnostics (Basel) 2023; 13:diagnostics13040806. [PMID: 36832294 PMCID: PMC9956028 DOI: 10.3390/diagnostics13040806] [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: 01/10/2023] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis is of particular importance to controlling and preventing the disease from spreading to other tissues. Artificial intelligence and machine learning have effectively detected and graded several cancers, in particular prostate cancer. The purpose of this review is to show the diagnostic performance (accuracy and area under the curve) of supervised machine learning algorithms in detecting prostate cancer using multiparametric MRI. A comparison was made between the performances of different supervised machine-learning methods. This review study was performed on the recent literature sourced from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up to the end of January 2023. The findings of this review reveal that supervised machine learning techniques have good performance with high accuracy and area under the curve for prostate cancer diagnosis and prediction using multiparametric MR imaging. Among supervised machine learning methods, deep learning, random forest, and logistic regression algorithms appear to have the best performance.
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16
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Rouvière O, Jaouen T, Baseilhac P, Benomar ML, Escande R, Crouzet S, Souchon R. Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review. Diagn Interv Imaging 2022; 104:221-234. [PMID: 36517398 DOI: 10.1016/j.diii.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The purpose of this study was to perform a systematic review of the literature on the diagnostic performance, in independent test cohorts, of artificial intelligence (AI)-based algorithms aimed at characterizing/detecting prostate cancer on magnetic resonance imaging (MRI). MATERIALS AND METHODS Medline, Embase and Web of Science were searched for studies published between January 2018 and September 2022, using a histological reference standard, and assessing prostate cancer characterization/detection by AI-based MRI algorithms in test cohorts composed of more than 40 patients and with at least one of the following independency criteria as compared to the training cohort: different institution, different population type, different MRI vendor, different magnetic field strength or strict temporal splitting. RESULTS Thirty-five studies were selected. The overall risk of bias was low. However, 23 studies did not use predefined diagnostic thresholds, which may have optimistically biased the results. Test cohorts fulfilled one to three of the five independency criteria. The diagnostic performance of the algorithms used as standalones was good, challenging that of human reading. In the 12 studies with predefined diagnostic thresholds, radiomics-based computer-aided diagnosis systems (assessing regions-of-interest drawn by the radiologist) tended to provide more robust results than deep learning-based computer-aided detection systems (providing probability maps). Two of the six studies comparing unassisted and assisted reading showed significant improvement due to the algorithm, mostly by reducing false positive findings. CONCLUSION Prostate MRI AI-based algorithms showed promising results, especially for the relatively simple task of characterizing predefined lesions. The best management of discrepancies between human reading and algorithm findings still needs to be defined.
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Affiliation(s)
- Olivier Rouvière
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France; Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France.
| | | | - Pierre Baseilhac
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Mohammed Lamine Benomar
- LabTAU, INSERM, U1032, Lyon 69003, France; University of Ain Temouchent, Faculty of Science and Technology, Algeria
| | - Raphael Escande
- Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Vascular and Urinary Imaging, Lyon 69003, France
| | - Sébastien Crouzet
- Université Lyon 1, Faculté de médecine Lyon Est, Lyon 69003, France; LabTAU, INSERM, U1032, Lyon 69003, France; Hospices Civils de Lyon, Hôpital Edouard Herriot, Department of Urology, Lyon 69003, France
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Liu X, Elbanan MG, Luna A, Haider MA, Smith AD, Sabottke CF, Spieler BM, Turkbey B, Fuentes D, Moawad A, Kamel S, Horvat N, Elsayes KM. Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status. AJR Am J Roentgenol 2022; 219:985-995. [PMID: 35766531 PMCID: PMC10616929 DOI: 10.2214/ajr.22.27695] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.
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Affiliation(s)
- Xiaoyang Liu
- Joint Department of Medical Imaging, Division of Abdominal Imaging, University Health Network, University of Toronto, ON, Canada
| | - Mohamed G Elbanan
- Department of Radiology, Yale New Haven Health, Bridgeport Hospital, Bridgeport, CT
| | | | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ
| | - Bradley M Spieler
- Department of Radiology, University Medical Center, Louisiana State University Health Sciences Center, New Orleans, LA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD
| | - David Fuentes
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ahmed Moawad
- Department of Diagnostic and Interventional Radiology, Mercy Catholic Medical Center, Darby, PA
| | - Serageldin Kamel
- Department of Lymphoma, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Khaled M Elsayes
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030
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18
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Abstract
Prostate MRI is now established as a first-line investigation for individuals presenting with suspected localized or locally advanced prostate cancer. Successful delivery of the MRI-directed pathway for prostate cancer diagnosis relies on high-quality imaging as well as the interpreting radiologist's experience and expertise. Radiologist certification in prostate MRI may help limit interreader variability, optimize outcomes, and provide individual radiologists with documentation of meeting predefined standards. This AJR Expert Panel Narrative Review summarizes existing certification proposals, recognizing variable progress across regions in establishing prostate MRI certification programs. To our knowledge, Germany is the only country with a prostate MRI certification process that is currently available for radiologists. However, prostate MRI certification programs have also recently been proposed in the United States and United Kingdom and by European professional society consensus panels. Recommended qualification processes entail a multifaceted approach, incorporating components such as minimum case numbers, peer learning, course participation, continuing medical education credits, and feedback from pathology results. Given the diversity in health care systems, including in the provision and availability of MRI services, national organizations will likely need to take independent approaches to certification and accreditation. The relevant professional organizations should begin developing these programs or continue existing plans for implementation.
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19
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Roest C, Fransen SJ, Kwee TC, Yakar D. Comparative Performance of Deep Learning and Radiologists for the Diagnosis and Localization of Clinically Significant Prostate Cancer at MRI: A Systematic Review. Life (Basel) 2022; 12:life12101490. [PMID: 36294928 PMCID: PMC9605624 DOI: 10.3390/life12101490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Deep learning (DL)-based models have demonstrated an ability to automatically diagnose clinically significant prostate cancer (PCa) on MRI scans and are regularly reported to approach expert performance. The aim of this work was to systematically review the literature comparing deep learning (DL) systems to radiologists in order to evaluate the comparative performance of current state-of-the-art deep learning models and radiologists. Methods: This systematic review was conducted in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Studies investigating DL models for diagnosing clinically significant (cs) PCa on MRI were included. The quality and risk of bias of each study were assessed using the checklist for AI in medical imaging (CLAIM) and QUADAS-2, respectively. Patient level and lesion-based diagnostic performance were separately evaluated by comparing the sensitivity achieved by DL and radiologists at an identical specificity and the false positives per patient, respectively. Results: The final selection consisted of eight studies with a combined 7337 patients. The median study quality with CLAIM was 74.1% (IQR: 70.6–77.6). DL achieved an identical patient-level performance to the radiologists for PI-RADS ≥ 3 (both 97.7%, SD = 2.1%). DL had a lower sensitivity for PI-RADS ≥ 4 (84.2% vs. 88.8%, p = 0.43). The sensitivity of DL for lesion localization was also between 2% and 12.5% lower than that of the radiologists. Conclusions: DL models for the diagnosis of csPCa on MRI appear to approach the performance of experts but currently have a lower sensitivity compared to experienced radiologists. There is a need for studies with larger datasets and for validation on external data.
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20
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Lophatananon A, Byrne MHV, Barrett T, Warren A, Muir K, Dokubo I, Georgiades F, Sheba M, Bibby L, Gnanapragasam VJ. Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population. BMC Cancer 2022; 22:878. [PMID: 35953766 PMCID: PMC9367076 DOI: 10.1186/s12885-022-09955-w] [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: 04/02/2022] [Accepted: 07/31/2022] [Indexed: 11/30/2022] Open
Abstract
Introduction Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population. Patient and methods Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm. Results Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame). Conclusion MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.
Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-09955-w.
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Affiliation(s)
- Artitaya Lophatananon
- Division of Population Health, Health Services Research & Primary Care Centre, University of Manchester, Manchester, UK
| | - Matthew H V Byrne
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Anne Warren
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Kenneth Muir
- Division of Population Health, Health Services Research & Primary Care Centre, University of Manchester, Manchester, UK
| | - Ibifuro Dokubo
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Fanos Georgiades
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK
| | - Mostafa Sheba
- Kasr Al Any School of Medicine, Cairo University, Giza, Egypt
| | - Lisa Bibby
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Vincent J Gnanapragasam
- Department of Urology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. .,Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK. .,Cambridge Urology Translational Research and Clinical Trials Office, Addenbrooke's Hospital, Cambridge Biomedical Campus, Cambridge, UK.
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21
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Dwivedi DK, Jagannathan NR. Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. MAGMA (NEW YORK, N.Y.) 2022; 35:587-608. [PMID: 35867236 DOI: 10.1007/s10334-022-01031-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 06/28/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, such as the increased false positive rate, inability to detect clinically significant prostate cancer (PCa) with random biopsy, multifocality in PCa, and the molecular heterogeneity of PCa, can be addressed by integrating advanced multiparametric MR imaging (mpMRI) approaches into the diagnostic workup of PCa. The standard method for diagnosing PCa is a transrectal ultrasonography (TRUS)-guided systematic prostate biopsy, but it suffers from sampling errors and frequently fails to detect clinically significant PCa. mpMRI not only increases the detection of clinically significant PCa, but it also helps to reduce unnecessary biopsies because of its high negative predictive value. Furthermore, non-Cartesian image acquisition and compressed sensing have resulted in faster MR acquisition with improved signal-to-noise ratio, which can be used in quantitative MRI methods such as dynamic contrast-enhanced (DCE)-MRI. With the growing emphasis on the role of pre-biopsy mpMRI in the evaluation of PCa, there is an increased demand for innovative MRI methods that can improve PCa grading, detect clinically significant PCa, and biopsy guidance. To meet these demands, in addition to routine T1-weighted, T2-weighted, DCE-MRI, diffusion MRI, and MR spectroscopy, several new MR methods such as restriction spectrum imaging, vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) method, hybrid multi-dimensional MRI, luminal water imaging, and MR fingerprinting have been developed for a better characterization of the disease. Further, with the increasing interest in combining MR data with clinical and genomic data, there is a growing interest in utilizing radiomics and radiogenomics approaches. These big data can also be utilized in the development of computer-aided diagnostic tools, including automatic segmentation and the detection of clinically significant PCa using machine learning methods.
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Affiliation(s)
- Durgesh Kumar Dwivedi
- Department of Radiodiagnosis, King George Medical University, Lucknow, UP, 226 003, India.
| | - Naranamangalam R Jagannathan
- Department of Radiology, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, TN, 603 103, India.
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, TN, 600 116, India.
- Department of Electrical Engineering, Indian Institute Technology Madras, Chennai, TN, 600 036, India.
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22
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Adams LC, Makowski MR, Engel G, Rattunde M, Busch F, Asbach P, Niehues SM, Vinayahalingam S, van Ginneken B, Litjens G, Bressem KK. Prostate158 - An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Comput Biol Med 2022; 148:105817. [PMID: 35841780 DOI: 10.1016/j.compbiomed.2022.105817] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/12/2022] [Accepted: 07/03/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND The development of deep learning (DL) models for prostate segmentation on magnetic resonance imaging (MRI) depends on expert-annotated data and reliable baselines, which are often not publicly available. This limits both reproducibility and comparability. METHODS Prostate158 consists of 158 expert annotated biparametric 3T prostate MRIs comprising T2w sequences and diffusion-weighted sequences with apparent diffusion coefficient maps. Two U-ResNets trained for segmentation of anatomy (central gland, peripheral zone) and suspicious lesions for prostate cancer (PCa) with a PI-RADS score of ≥4 served as baseline algorithms. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), the Hausdorff distance (HD), and the average surface distance (ASD). The Wilcoxon test with Bonferroni correction was used to evaluate differences in performance. The generalizability of the baseline model was assessed using the open datasets Medical Segmentation Decathlon and PROSTATEx. RESULTS Compared to Reader 1, the models achieved a DSC/HD/ASD of 0.88/18.3/2.2 for the central gland, 0.75/22.8/1.9 for the peripheral zone, and 0.45/36.7/17.4 for PCa. Compared with Reader 2, the DSC/HD/ASD were 0.88/17.5/2.6 for the central gland, 0.73/33.2/1.9 for the peripheral zone, and 0.4/39.5/19.1 for PCa. Interrater agreement measured in DSC/HD/ASD was 0.87/11.1/1.0 for the central gland, 0.75/15.8/0.74 for the peripheral zone, and 0.6/18.8/5.5 for PCa. Segmentation performances on the Medical Segmentation Decathlon and PROSTATEx were 0.82/22.5/3.4; 0.86/18.6/2.5 for the central gland, and 0.64/29.2/4.7; 0.71/26.3/2.2 for the peripheral zone. CONCLUSIONS We provide an openly accessible, expert-annotated 3T dataset of prostate MRI and a reproducible benchmark to foster the development of prostate segmentation algorithms.
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Affiliation(s)
- Lisa C Adams
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
| | - Marcus R Makowski
- Technical University of Munich, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Ismaninger Str. 22, 81675, Munich, Germany
| | - Günther Engel
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Institute for Diagnostic and Interventional Radiology, Georg-August University, Göttingen, Germany
| | - Maximilian Rattunde
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Felix Busch
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Patrick Asbach
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Stefan M Niehues
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Center, Nijmegen, GA, the Netherlands
| | | | - Geert Litjens
- Radboud University Medical Center, Nijmegen, GA, the Netherlands
| | - Keno K Bressem
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Luisenstraße 7, 10117, Hindenburgdamm 30, 12203, Berlin, Germany; Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
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Sushentsev N, Barrett T. The role of artificial intelligence in MRI-driven active surveillance in prostate cancer. Nat Rev Urol 2022; 19:510. [PMID: 35715704 DOI: 10.1038/s41585-022-00619-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
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