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Couchoux T, Jaouen T, Melodelima-Gonindard C, Baseilhac P, Branchu A, Arfi N, Aziza R, Barry Delongchamps N, Bladou F, Bratan F, Brunelle S, Colin P, Correas JM, Cornud F, Descotes JL, Eschwege P, Fiard G, Guillaume B, Grange R, Grenier N, Lang H, Lefèvre F, Malavaud B, Marcelin C, Moldovan PC, Mottet N, Mozer P, Potiron E, Portalez D, Puech P, Renard-Penna R, Roumiguié M, Roy C, Timsit MO, Tricard T, Villers A, Walz J, Debeer S, Mansuy A, Mège-Lechevallier F, Decaussin-Petrucci M, Badet L, Colombel M, Ruffion A, Crouzet S, Rabilloud M, Souchon R, Rouvière O. Performance of a Region of Interest-based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database-CAD-FIRST Study. Eur Urol Oncol 2024; 7:1113-1122. [PMID: 38493072 DOI: 10.1016/j.euo.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI. METHODS The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively. KEY FINDINGS AND LIMITATIONS After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment. CONCLUSIONS AND CLINICAL IMPLICATIONS The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy. PATIENT SUMMARY An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.
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Affiliation(s)
- Thibaut Couchoux
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | | | - Pierre Baseilhac
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Arthur Branchu
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Nicolas Arfi
- Department of Urology, Hôpital Saint Joseph Saint Luc, Lyon, France
| | - Richard Aziza
- Department of Radiology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | | | - Franck Bladou
- Department of Urology, Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
| | - Flavie Bratan
- Department of Diagnostic and Interventional Imaging, Hôpital Saint Joseph Saint Luc, Lyon, France
| | - Serge Brunelle
- Department of Radiology and Medical Imaging, Institut Paoli-Calmettes Cancer Center, Marseille, France
| | - Pierre Colin
- Department of Urology, Hôpital privé La Louvrière, Lille, France
| | - Jean-Michel Correas
- Department of Radiology, Hôpital Necker, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - François Cornud
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Jean-Luc Descotes
- Université Grenoble Alpes, Grenoble, France; Department of Urology, Centre Hospitalier Universitaire de Grenoble, Grenoble, France
| | - Pascal Eschwege
- Department of Urology, Centre Hospitalier Régional et Universitaire de Nancy, Vandoeuvre, France
| | - Gaelle Fiard
- Université Grenoble Alpes, Grenoble, France; Department of Urology, Centre Hospitalier Universitaire de Grenoble, Grenoble, France
| | - Bénédicte Guillaume
- Department of Radiology, Centre Hospitalier Universitaire de Grenoble, Université Grenoble Apes, Grenoble, France
| | - Rémi Grange
- Department of Radiology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France
| | - Nicolas Grenier
- Department of Radiology, Centre Hospitalier Universitaire de Bordeaux, Hôpital Pellegrin, Bordeaux, France
| | - Hervé Lang
- Department of Urology, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Frédéric Lefèvre
- Department of Radiology, Centre Hospitalier Régional et Universitaire de Nancy, Vandoeuvre, France
| | - Bernard Malavaud
- Department of Urology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Clément Marcelin
- Department of Radiology, Centre Hospitalier Universitaire de Bordeaux, Hôpital Pellegrin, Bordeaux, France
| | - Paul C Moldovan
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Nicolas Mottet
- Department of Urology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France
| | - Pierre Mozer
- Department of Urology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Eric Potiron
- Clinique Urologique de Nantes, Saint-Herblain, France
| | - Daniel Portalez
- Department of Radiology, Institut Universitaire du Cancer de Toulouse, Toulouse, France
| | - Philippe Puech
- Department of Radiology, Centre Hospitalier Régional et Universitaire de Lille, Lille, France
| | - Raphaele Renard-Penna
- Department of Radiology, Hôpital Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Paris, France; GRC no 5, ONCOTYPE-URO, Sorbonne Universités, Paris, France
| | - Matthieu Roumiguié
- Department of Urology, Toulouse-Rangueil University Hospital, Toulouse France
| | - Catherine Roy
- Department of Radiology B, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Marc-Olivier Timsit
- Department of Urology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Thibault Tricard
- Department of Urology, Centre Hospitalier Universitaire de Strasbourg, Nouvel Hôpital Civil, Strasbourg, France
| | - Arnauld Villers
- Department of Urology, Univ. Lille, CHU Lille, Lille, France
| | - Jochen Walz
- Department of Urology, Institut Paoli-Calmettes Cancer Center, Marseille, France
| | - Sabine Debeer
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | - Adeline Mansuy
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France
| | | | | | - Lionel Badet
- Department of Urology, University Hospital of Saint-Etienne, Saint-Priest-en-Jarez, France; Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Marc Colombel
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Alain Ruffion
- Université Lyon 1, Université de Lyon, Lyon, France; Department of Urology, Centre Hospitalier Lyon Sud, Hospices Cibvils de Lyon, Pierre-Bénite, France
| | - Sébastien Crouzet
- LabTau, INSERM Unit 1032, Lyon, France; Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France
| | - Muriel Rabilloud
- Université Lyon 1, Université de Lyon, Lyon, France; Pôle Santé Publique, Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Lyon, France; CNRS, UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, Équipe Biostatistique-Santé, Villeurbanne, France
| | | | - Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Lyon, France; LabTau, INSERM Unit 1032, Lyon, France; Université Lyon 1, Université de Lyon, Lyon, France.
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Correia ETDO, Baydoun A, Li Q, Costa DN, Bittencourt LK. Emerging and anticipated innovations in prostate cancer MRI and their impact on patient care. Abdom Radiol (NY) 2024; 49:3696-3710. [PMID: 38877356 PMCID: PMC11390809 DOI: 10.1007/s00261-024-04423-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Revised: 05/27/2024] [Accepted: 05/28/2024] [Indexed: 06/16/2024]
Abstract
Prostate cancer (PCa) remains the leading malignancy affecting men, with over 3 million men living with the disease in the US, and an estimated 288,000 new cases and almost 35,000 deaths in 2023 in the United States alone. Over the last few decades, imaging has been a cornerstone in PCa care, with a crucial role in the detection, staging, and assessment of PCa recurrence or by guiding diagnostic or therapeutic interventions. To improve diagnostic accuracy and outcomes in PCa care, remarkable advancements have been made to different imaging modalities in recent years. This paper focuses on reviewing the main innovations in the field of PCa magnetic resonance imaging, including MRI protocols, MRI-guided procedural interventions, artificial intelligence algorithms and positron emission tomography, which may impact PCa care in the future.
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Affiliation(s)
| | - Atallah Baydoun
- Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Qiubai Li
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Daniel N Costa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Leonardo Kayat Bittencourt
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
- Department of Radiology, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA.
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Tsuboyama T, Yanagawa M, Fujioka T, Fujita S, Ueda D, Ito R, Yamada A, Fushimi Y, Tatsugami F, Nakaura T, Nozaki T, Kamagata K, Matsui Y, Hirata K, Fujima N, Kawamura M, Naganawa S. Recent trends in AI applications for pelvic MRI: a comprehensive review. LA RADIOLOGIA MEDICA 2024; 129:1275-1287. [PMID: 39096356 DOI: 10.1007/s11547-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/25/2024] [Indexed: 08/05/2024]
Abstract
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
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Affiliation(s)
- Takahiro Tsuboyama
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan.
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Daiju Ueda
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Akira Yamada
- Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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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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Penzkofer T. Prostate-MRI reporting should be done with the aid of AI systems: Pros. Eur Radiol 2024:10.1007/s00330-024-10909-y. [PMID: 38981893 DOI: 10.1007/s00330-024-10909-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 05/27/2024] [Accepted: 06/08/2024] [Indexed: 07/11/2024]
Affiliation(s)
- Tobias Penzkofer
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health, Berlin, Germany.
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Batool A, Byun YC. Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions. Comput Biol Med 2024; 175:108412. [PMID: 38691914 DOI: 10.1016/j.compbiomed.2024.108412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/18/2024] [Accepted: 04/02/2024] [Indexed: 05/03/2024]
Abstract
Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care.
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Affiliation(s)
- Amreen Batool
- Department of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju, 63243, South Korea
| | - Yung-Cheol Byun
- Department of Computer Engineering, Major of Electronic Engineering, Jeju National University, Institute of Information Science Technology, Jeju, 63243, South Korea.
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7
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Huang YY, Deng YS, Liu Y, Qiang MY, Qiu WZ, Xia WX, Jing BZ, Feng CY, Chen HH, Cao X, Zhou JY, Huang HY, Zhan ZJ, Deng Y, Tang LQ, Mai HQ, Sun Y, Xie CM, Guo X, Ke LR, Lv X, Li CF. A deep learning-based semiautomated workflow for triaging follow-up MR scans in treated nasopharyngeal carcinoma. iScience 2023; 26:108347. [PMID: 38125021 PMCID: PMC10730347 DOI: 10.1016/j.isci.2023.108347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/08/2023] [Accepted: 10/24/2023] [Indexed: 12/23/2023] Open
Abstract
It is imperative to optimally utilize virtues and obviate defects of fully automated analysis and expert knowledge in new paradigms of healthcare. We present a deep learning-based semiautomated workflow (RAINMAN) with 12,809 follow-up scans among 2,172 patients with treated nasopharyngeal carcinoma from three centers (ChiCTR.org.cn, Chi-CTR2200056595). A boost of diagnostic performance and reduced workload was observed in RAINMAN compared with the original manual interpretations (internal vs. external: sensitivity, 2.5% [p = 0.500] vs. 3.2% [p = 0.031]; specificity, 2.9% [p < 0.001] vs. 0.3% [p = 0.302]; workload reduction, 79.3% vs. 76.2%). The workflow also yielded a triaging performance of 83.6%, with increases of 1.5% in sensitivity (p = 1.000) and 0.6%-1.3% (all p < 0.05) in specificity compared to three radiologists in the reader study. The semiautomated workflow shows its unique superiority in reducing radiologist's workload by eliminating negative scans while retaining the diagnostic performance of radiologists.
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Affiliation(s)
- Ying-Ying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Yi-Shu Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
- School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yang Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Meng-Yun Qiang
- Department of Radiation Oncology, Cancer Hospital of The University of Chinese Academy of Sciences, Hangzhou 310005, China
| | - Wen-Ze Qiu
- Department of Radiation Oncology, The Affiliated Cancer Hospital of Guangzhou Medical University, Guangzhou 510095, China
| | - Wei-Xiong Xia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Bing-Zhong Jing
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chen-Yang Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hao-Hua Chen
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xun Cao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Critical Care Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Jia-Yu Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hao-Yang Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ze-Jiang Zhan
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ying Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Lin-Quan Tang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Hai-Qiang Mai
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Ying Sun
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chuan-Miao Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xiang Guo
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Liang-Ru Ke
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Xing Lv
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Chao-Feng Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China
- Department of Information, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
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Zhao H, Zheng C, Zhang H, Rao M, Li Y, Fang D, Huang J, Zhang W, Yuan G. Diagnosis of thyroid disease using deep convolutional neural network models applied to thyroid scintigraphy images: a multicenter study. Front Endocrinol (Lausanne) 2023; 14:1224191. [PMID: 37635985 PMCID: PMC10453808 DOI: 10.3389/fendo.2023.1224191] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Objectives The aim of this study was to improve the diagnostic performance of nuclear medicine physicians using a deep convolutional neural network (DCNN) model and validate the results with two multicenter datasets for thyroid disease by analyzing clinical single-photon emission computed tomography (SPECT) image data. Methods In this multicenter retrospective study, 3194 SPECT thyroid images were collected for model training (n=2067), internal validation (n=514) and external validation (n=613). First, four pretrained DCNN models (AlexNet, ShuffleNetV2, MobileNetV3 and ResNet-34) for were tested multiple medical image classification of thyroid disease types (i.e., Graves' disease, subacute thyroiditis, thyroid tumor and normal thyroid). The best performing model was then subjected to fivefold cross-validation to further assess its performance, and the diagnostic performance of this model was compared with that of junior and senior nuclear medicine physicians. Finally, class-specific attentional regions were visualized with attention heatmaps using gradient-weighted class activation mapping. Results Each of the four pretrained neural networks attained an overall accuracy of more than 0.85 for the classification of SPECT thyroid images. The improved ResNet-34 model performed best, with an accuracy of 0.944. For the internal validation set, the ResNet-34 model showed higher accuracy (p < 0.001) when compared to that of the senior nuclear medicine physician, with an improvement of nearly 10%. Our model achieved an overall accuracy of 0.931 for the external dataset, a significantly higher accuracy than that of the senior physician (0.931 vs. 0.868, p < 0.001). Conclusion The DCNN-based model performed well in terms of diagnosing thyroid scintillation images. The DCNN model showed higher sensitivity and greater specificity in identifying Graves' disease, subacute thyroiditis, and thyroid tumors compared to those of nuclear medicine physicians, illustrating the feasibility of deep learning models to improve the diagnostic efficiency for assisting clinicians.
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Liu X, Shi J, Li Z, Huang Y, Zhang Z, Zhang C. The Present and Future of Artificial Intelligence in Urological Cancer. J Clin Med 2023; 12:4995. [PMID: 37568397 PMCID: PMC10419644 DOI: 10.3390/jcm12154995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
Artificial intelligence has drawn more and more attention for both research and application in the field of medicine. It has considerable potential for urological cancer detection, therapy, and prognosis prediction due to its ability to choose features in data to complete a particular task autonomously. Although the clinical application of AI is still immature and faces drawbacks such as insufficient data and a lack of prospective clinical trials, AI will play an essential role in individualization and the whole management of cancers as research progresses. In this review, we summarize the applications and studies of AI in major urological cancers, including tumor diagnosis, treatment, and prognosis prediction. Moreover, we discuss the current challenges and future applications of AI.
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Affiliation(s)
| | | | | | | | - Zhihong Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
| | - Changwen Zhang
- Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China; (X.L.)
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Turkbey B, Purysko AS. PI-RADS: Where Next? Radiology 2023; 307:e223128. [PMID: 37097134 PMCID: PMC10315529 DOI: 10.1148/radiol.223128] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 04/26/2023]
Abstract
Prostate MRI plays an important role in the clinical management of localized prostate cancer, mainly assisting in biopsy decisions and guiding biopsy procedures. The Prostate Imaging Reporting and Data System (PI-RADS) has been available to radiologists since 2012, with the most up-to-date and actively used version being PI-RADS version 2.1. This review article discusses the current use of PI-RADS, including its limitations and controversies, and summarizes research that aims to improve future iterations of this system.
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Affiliation(s)
- Baris Turkbey
- From the Molecular Imaging Branch, National Cancer Institute,
National Institutes of Health, 10 Center Dr, MSC 1182, Building 10, Room B3B85,
Bethesda, MD 20892 (B.T.); and Section of Abdominal Imaging, Department of
Nuclear Radiology, Cleveland Clinic Imaging Institute, Cleveland, Ohio
(A.S.P.)
| | - Andrei S. Purysko
- From the Molecular Imaging Branch, National Cancer Institute,
National Institutes of Health, 10 Center Dr, MSC 1182, Building 10, Room B3B85,
Bethesda, MD 20892 (B.T.); and Section of Abdominal Imaging, Department of
Nuclear Radiology, Cleveland Clinic Imaging Institute, Cleveland, Ohio
(A.S.P.)
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11
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Sun Z, Wang K, Kong Z, Xing Z, Chen Y, Luo N, Yu Y, Song B, Wu P, Wang X, Zhang X, Wang X. A multicenter study of artificial intelligence-aided software for detecting visible clinically significant prostate cancer on mpMRI. Insights Imaging 2023; 14:72. [PMID: 37121983 PMCID: PMC10149551 DOI: 10.1186/s13244-023-01421-w] [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: 11/19/2022] [Accepted: 04/05/2023] [Indexed: 05/02/2023] Open
Abstract
BACKGROUND AI-based software may improve the performance of radiologists when detecting clinically significant prostate cancer (csPCa). This study aims to compare the performance of radiologists in detecting MRI-visible csPCa on MRI with and without AI-based software. MATERIALS AND METHODS In total, 480 multiparametric MRI (mpMRI) images were retrospectively collected from eleven different MR devices, with 349 csPCa lesions in 180 (37.5%) cases. The csPCa areas were annotated based on pathology. Sixteen radiologists from four hospitals participated in reading. Each radiologist was randomly assigned to 30 cases and diagnosed twice. Half cases were interpreted without AI, and the other half were interpreted with AI. After four weeks, the cases were read again in switched mode. The mean diagnostic performance was compared using sensitivity and specificity on lesion level and patient level. The median reading time and diagnostic confidence were assessed. RESULTS On lesion level, AI-aided improved the sensitivity from 40.1% to 59.0% (18.9% increased; 95% confidence interval (CI) [11.5, 26.1]; p < .001). On patient level, AI-aided improved the specificity from 57.7 to 71.7% (14.0% increase, 95% CI [6.4, 21.4]; p < .001) while preserving the sensitivity (88.3% vs. 93.9%, p = 0.06). AI-aided reduced the median reading time of one case by 56.3% from 423 to 185 s (238-s decrease, 95% CI [219, 260]; p < .001), and the median diagnostic confidence score was increased by 10.3% from 3.9 to 4.3 (0.4-score increase, 95% CI [0.3, 0.5]; p < .001). CONCLUSIONS AI software improves the performance of radiologists by reducing false positive detection of prostate cancer patients and also improving reading times and diagnostic confidence. CLINICAL RELEVANCE STATEMENT This study involves the process of data collection, randomization and crossover reading procedure.
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Affiliation(s)
- Zhaonan Sun
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Kexin Wang
- School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Zixuan Kong
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Zhangli Xing
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Yuntian Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ning Luo
- Department of Radiology, The Second Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China
| | - Yang Yu
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Pengsheng Wu
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiangpeng Wang
- Beijing Smart Tree Medical Technology Co. Ltd., Beijing, China
| | - Xiaodong Zhang
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China
| | - Xiaoying Wang
- Department of Radiology, Peking University First Hospital, No.8 Xishiku Street, Xicheng District, Beijing, 100034, China.
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Di Franco F, Souchon R, Crouzet S, Colombel M, Ruffion A, Klich A, Almeras M, Milot L, Rabilloud M, Rouvière O. Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study). Insights Imaging 2023; 14:49. [PMID: 36939970 PMCID: PMC10027981 DOI: 10.1186/s13244-023-01391-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 02/15/2023] [Indexed: 03/21/2023] Open
Abstract
OBJECTIVE To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience. METHODS Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement. RESULTS At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results. CONCLUSIONS Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.
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Affiliation(s)
- Florian Di Franco
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
| | | | - Sébastien Crouzet
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Marc Colombel
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Est, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Hôpital Edouard Herriot, 69437, Lyon, France
| | - Alain Ruffion
- Université de Lyon, Université Lyon 1, Lyon, France
- Hospices Civils de Lyon, Department of Urology, Centre Hospitalier Lyon Sud, Pierre-Bénite, France
- Equipe 2-Centre d'Innovation en Cancérologie de Lyon, 3738, Lyon, EA, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Amna Klich
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Mathilde Almeras
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Laurent Milot
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France
- INSERM, LabTau, U1032, Lyon, France
- Université de Lyon, Université Lyon 1, Lyon, France
- Faculté de Médecine Lyon Sud, 69003, Lyon, France
| | - Muriel Rabilloud
- Université de Lyon, Université Lyon 1, Lyon, France
- Service de Biostatistique et Bioinformatique, Hospices Civils de Lyon, Pôle Santé Publique, 69003, Lyon, France
- UMR 5558, Laboratoire de Biométrie et Biologie Évolutive, CNRS, Équipe Biostatistique-Santé, 69100, Villeurbanne, France
| | - Olivier Rouvière
- Hospices Civils de Lyon, Department of Imaging, Hôpital Edouard Herriot, 69437, Lyon, France.
- INSERM, LabTau, U1032, Lyon, France.
- Université de Lyon, Université Lyon 1, Lyon, France.
- Faculté de Médecine Lyon Est, Lyon, France.
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Giganti F, Panebianco V, Tempany CM, Purysko AS. Is Artificial Intelligence Replacing Our Radiology Stars in Prostate Magnetic Resonance Imaging? The Stars Do Not Look Big, But They Can Look Brighter. EUR UROL SUPPL 2023; 48:12-13. [PMID: 36578461 PMCID: PMC9791603 DOI: 10.1016/j.euros.2022.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Affiliation(s)
- Francesco Giganti
- Department of Radiology, University College London Hospital NHS Foundation Trust, London, UK
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Valeria Panebianco
- Department of Radiological Sciences, Oncology and Pathology, Sapienza University/Policlinico Umberto I, Rome, Italy
| | - Clare M. Tempany
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrei S. Purysko
- Abdominal Imaging Section and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
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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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