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Tong A, Bagga B, Petrocelli R, Smereka P, Vij A, Qian K, Grimm R, Kamen A, Keerthivasan MB, Nickel MD, von Busch H, Chandarana H. Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate. J Magn Reson Imaging 2023; 58:1055-1064. [PMID: 36651358 PMCID: PMC10352465 DOI: 10.1002/jmri.28602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI). PURPOSE To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer. STUDY TYPE Retrospective. POPULATION Eighty consecutive men, mean age 66 years (47-84) with suspected prostate cancer or prostate cancer on active surveillance who had a prostate MRI from December 28, 2020 to April 28, 2021 were included. Follow-up included prostate biopsy or stability of prostate-specific antigen (PSA) for 1 year. FIELD STRENGTH AND SEQUENCES A 3 T MRI. Conventional axial and coronal T2 turbo spin echo (CL-T2), 3-fold deep learning-accelerated axial and coronal T2-weighted sequence (DL-T2), diffusion weighted imaging (DWI) with b = 50 sec/mm2 , 1000 sec/mm2 , calculated b = 1500 sec/mm2 . ASSESSMENT CL-bpMRI and DL-bpMRI including the same conventional diffusion-weighted imaging (DWI) were presented to three radiologists (blinded to acquisition method) and to a deep learning computer-assisted detection algorithm (DL-CAD). The readers evaluated image quality using a 4-point Likert scale (1 = nondiagnostic, 4 = excellent) and graded lesions using Prostate Imaging Reporting and Data System (PI-RADS) v2.1. DL-CAD identified and assigned lesions of PI-RADS 3 or greater. STATISTICAL TESTS Quality metrics were compared using Wilcoxon signed rank test, and area under the receiver operating characteristic curve (AUC) were compared using Delong's test. SIGNIFICANCE P = 0.05. RESULTS Eighty men were included (age: 66 ± 9 years; 17/80 clinically significant prostate cancer). Overall image quality results by the three readers (CL-T2, DL-T2) are reader 1: 3.72 ± 0.53, 3.89 ± 0.39 (P = 0.99); reader 2: 3.33 ± 0.82, 3.31 ± 0.74 (P = 0.49); reader 3: 3.67 ± 0.63, 3.51 ± 0.62. In the patient-based analysis, the reader results of AUC are (CL-bpMRI, DL-bpMRI): reader 1: 0.77, 0.78 (P = 0.98), reader 2: 0.65, 0.66 (P = 0.99), reader 3: 0.57, 0.60 (P = 0.52). Diagnostic statistics from DL-CAD (CL-bpMRI, DL-bpMRI) are sensitivity (0.71, 0.71, P = 1.00), specificity (0.59, 0.44, P = 0.05), positive predictive value (0.23, 0.24, P = 0.25), negative predictive value (0.88, 0.88, P = 0.48). CONCLUSION Deep learning-accelerated T2-weighted imaging may potentially be used to decrease acquisition time for bpMRI. EVIDENCE LEVEL 3. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Angela Tong
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Barun Bagga
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Robert Petrocelli
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Paul Smereka
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Abhinav Vij
- Department of Radiology, NYU Langone Health, New York, New York, USA
| | - Kun Qian
- Division of Biostatistics, Department of Population Health, Grossman School of Medicine, NYU Langone Health, New York, New York, USA
| | - Robert Grimm
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Ali Kamen
- Digital Technology and Innovation, Siemens Healthineers, Princeton, New Jersey, USA
| | | | | | | | - Hersh Chandarana
- Department of Radiology, NYU Langone Health, New York, New York, USA
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Lin DJ, Schwier M, Geiger B, Raithel E, von Busch H, Fritz J, Kline M, Brooks M, Dunham K, Shukla M, Alaia EF, Samim M, Joshi V, Walter WR, Ellermann JM, Ilaslan H, Rubin D, Winalski CS, Recht MP. Deep Learning Diagnosis and Classification of Rotator Cuff Tears on Shoulder MRI. Invest Radiol 2023; 58:405-412. [PMID: 36728041 DOI: 10.1097/rli.0000000000000951] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Detection of rotator cuff tears, a common cause of shoulder disability, can be time-consuming and subject to reader variability. Deep learning (DL) has the potential to increase radiologist accuracy and consistency. PURPOSE The aim of this study was to develop a prototype DL model for detection and classification of rotator cuff tears on shoulder magnetic resonance imaging into no tear, partial-thickness tear, or full-thickness tear. MATERIALS AND METHODS This Health Insurance Portability and Accountability Act-compliant, institutional review board-approved study included a total of 11,925 noncontrast shoulder magnetic resonance imaging scans from 2 institutions, with 11,405 for development and 520 dedicated for final testing. A DL ensemble algorithm was developed that used 4 series as input from each examination: fluid-sensitive sequences in 3 planes and a sagittal oblique T1-weighted sequence. Radiology reports served as ground truth for training with categories of no tear, partial tear, or full-thickness tear. A multireader study was conducted for the test set ground truth, which was determined by the majority vote of 3 readers per case. The ensemble comprised 4 parallel 3D ResNet50 convolutional neural network architectures trained via transfer learning and then adapted to the targeted domain. The final tear-type prediction was determined as the class with the highest probability, after averaging the class probabilities of the 4 individual models. RESULTS The AUC overall for supraspinatus, infraspinatus, and subscapularis tendon tears was 0.93, 0.89, and 0.90, respectively. The model performed best for full-thickness supraspinatus, infraspinatus, and subscapularis tears with AUCs of 0.98, 0.99, and 0.95, respectively. Multisequence input demonstrated higher AUCs than single-sequence input for infraspinatus and subscapularis tendon tears, whereas coronal oblique fluid-sensitive and multisequence input showed similar AUCs for supraspinatus tendon tears. Model accuracy for tear types and overall accuracy were similar to that of the clinical readers. CONCLUSIONS Deep learning diagnosis of rotator cuff tears is feasible with excellent diagnostic performance, particularly for full-thickness tears, with model accuracy similar to subspecialty-trained musculoskeletal radiologists.
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Affiliation(s)
- Dana J Lin
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | | | | | | | | | - Jan Fritz
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Mitchell Kline
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Michael Brooks
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Kevin Dunham
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Mehool Shukla
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Erin F Alaia
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Mohammad Samim
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Vivek Joshi
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - William R Walter
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
| | - Jutta M Ellermann
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN
| | | | | | | | - Michael P Recht
- From the Department of Radiology, NYU Grossman School of Medicine, New York, NY
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3
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Oerther B, Engel H, Nedelcu A, Schlett CL, Grimm R, von Busch H, Sigle A, Gratzke C, Bamberg F, Benndorf M. Prediction of upgrade to clinically significant prostate cancer in patients under active surveillance: performance of a fully automated AI-algorithm for lesion detection and classification. Prostate 2023; 83:871-878. [PMID: 36959777 DOI: 10.1002/pros.24528] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/18/2023] [Accepted: 03/17/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Multiparametric MRI (MpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases of active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS. METHODS Consecutive patients under AS who received mpMRI (PI-RADSv2.1 protocol) and subsequent MR-guided ultrasound fusion (targeted and extensive systematic) biopsy between 2017 and 2020 were retrospectively analyzed. Diagnostic performance of an automated clinically certified AI-driven algorithm was evaluated on both lesion and patient level regarding the detection of csPCa. RESULTS Analysis of 56 patients resulted in 93 target lesions. Patient level sensitivity and specificity of the AI algorithm was 92.5%/31% for the detection of ISUP ≥ 1 and 96.4%/25% for the detection of ISUP ≥ 2, respectively. The only case of csPCa missed by the AI harbored only 1/47 Gleason 7a core (systematic biopsy; previous and subsequent biopsies rendered non-csPCa). CONCLUSIONS AI-augmented lesion detection and PI-RADS scoring is a robust tool to detect progression to clinically significant PCa in patients under AS. Integration in the clinical workflow can serve as reassurance for the reader and streamline reporting, hence improve efficiency and diagnostic confidence. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Benedict Oerther
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Hannes Engel
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Andrea Nedelcu
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Christopher L Schlett
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Robert Grimm
- Diagnostic Imaging, Siemens Healthcare GmbH, 91052, Erlangen, Germany
| | | | - August Sigle
- Department of Urology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Christian Gratzke
- Department of Urology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany, 79106, Freiburg, Germany
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Hu L, Fu C, Song X, Grimm R, von Busch H, Benkert T, Kamen A, Lou B, Huisman H, Tong A, Penzkofer T, Choi MH, Shabunin I, Winkel D, Xing P, Szolar D, Coakley F, Shea S, Szurowska E, Guo JY, Li L, Li YH, Zhao JG. Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique. Cancer Imaging 2023; 23:6. [PMID: 36647150 PMCID: PMC9843860 DOI: 10.1186/s40644-023-00527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.
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Affiliation(s)
- Lei Hu
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Caixia Fu
- MR Application Development, Siemens Shenzhen magnetic Resonance Ltd., Shenzhen, China
| | - Xinyang Song
- grid.443573.20000 0004 1799 2448Department of Radiology, Xiangyang No.1 People’s Hospital, Hubei University of Medicine, Xiangyang, 441000 China
| | - Robert Grimm
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Heinrich von Busch
- grid.5406.7000000012178835XInnovation Owner Artificial Intelligence for Oncology, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- grid.5406.7000000012178835XMR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Ali Kamen
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Bin Lou
- grid.415886.60000 0004 0546 1113Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ USA
| | - Henkjan Huisman
- grid.10417.330000 0004 0444 9382Radboud University Medical Center, Nijmegen, Netherlands
| | - Angela Tong
- grid.137628.90000 0004 1936 8753New York University, New York City, NY USA
| | - Tobias Penzkofer
- grid.6363.00000 0001 2218 4662Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Moon Hyung Choi
- grid.411947.e0000 0004 0470 4224Eunpyeong St. Mary’s Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | | | - David Winkel
- grid.410567.1Universitätsspital Basel, Basel, Switzerland
| | - Pengyi Xing
- grid.411525.60000 0004 0369 1599Changhai Hospital, Shanghai, China
| | | | - Fergus Coakley
- grid.5288.70000 0000 9758 5690Oregon Health and Science University, Portland, OR USA
| | - Steven Shea
- grid.411451.40000 0001 2215 0876Loyola University Medical Center, Maywood, IL USA
| | - Edyta Szurowska
- grid.11451.300000 0001 0531 3426Medical University of Gdansk, Gdansk, Poland
| | - Jing-yi Guo
- grid.16821.3c0000 0004 0368 8293Clinical Research Center, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233 China
| | - Liang Li
- grid.412632.00000 0004 1758 2270Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, 430060 China
| | - Yue-hua Li
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
| | - Jun-gong Zhao
- grid.16821.3c0000 0004 0368 8293Department of Diagnostic and Interventional Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, No. 600, Yi Shan Road, Shanghai, 200233 China
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Youn SY, Choi MH, Lee YJ, Grimm R, von Busch H, Han D, Son Y, Lou B, Kamen A. Prostate gland volume estimation: anteroposterior diameters measured on axial versus sagittal ultrasonography and magnetic resonance images. Ultrasonography 2023; 42:154-164. [PMID: 36475357 PMCID: PMC9816709 DOI: 10.14366/usg.22104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/24/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The aim of this study was to evaluate the accuracy of prostate volume estimates calculated from the ellipsoid formula using the anteroposterior (AP) diameter measured on axial and sagittal images obtained through ultrasonography (US) and magnetic resonance imaging (MRI). METHODS This retrospective study included 456 patients with transrectal US and MRI from two university hospitals. Two radiologists independently measured the prostate gland diameters on US and MRI: AP diameters on axial and sagittal images, transverse, and longitudinal diameters on midsagittal images. The volume estimates, volumeax and volumesag, were calculated from the ellipsoid formula by using the AP diameter on axial and sagittal images, respectively. The prostate volume extracted from MRI-based whole-gland segmentation was considered the gold standard. The intraclass correlation coefficient (ICC) was used to evaluate the inter-method agreement between volumeax and volumesag, and agreement with the gold standard. The Wilcoxon signedrank test was used to analyze the differences between the volume estimates and the gold standard. RESULTS The prostate gland volume estimates showed excellent inter-method agreement, and excellent agreement with the gold standard (ICCs >0.9). Compared with the gold standard, the volume estimates were significantly larger on MRI and significantly smaller on US (P<0.001). The volume difference (segmented volume-volume estimate) was greater in patients with larger prostate glands, especially on US. CONCLUSION Volumeax and volumesag showed excellent inter-method agreement and excellent agreement with the gold standard on both US and MRI. However, prostate volume was overestimated on MRI and underestimated on US.
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Affiliation(s)
- Seo Yeon Youn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Moon Hyung Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea,Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea,Correspondence to: Moon Hyung Choi, MD, PhD, Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Tongil-ro, Eunpyeong-gu, Seoul 03312, Korea Tel. +82-2-2030-3013 Fax. +82-2-2030-3026 E-mail:
| | - Young Joon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea,Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Robert Grimm
- Diagnostic Imaging, Siemens Healthcare, Erlangen, Germany
| | | | | | - Yohan Son
- Siemens Healthineers Ltd., Seoul, Korea
| | - Bin Lou
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Ali Kamen
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
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Labus S, Altmann MM, Huisman H, Tong A, Penzkofer T, Choi MH, Shabunin I, Winkel DJ, Xing P, Szolar DH, Shea SM, Grimm R, von Busch H, Kamen A, Herold T, Baumann C. A concurrent, deep learning-based computer-aided detection system for prostate multiparametric MRI: a performance study involving experienced and less-experienced radiologists. Eur Radiol 2022; 33:64-76. [PMID: 35900376 DOI: 10.1007/s00330-022-08978-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/16/2022] [Accepted: 06/21/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the effect of a deep learning-based computer-aided diagnosis (DL-CAD) system on experienced and less-experienced radiologists in reading prostate mpMRI. METHODS In this retrospective, multi-reader multi-case study, a consecutive set of 184 patients examined between 01/2018 and 08/2019 were enrolled. Ground truth was combined targeted and 12-core systematic transrectal ultrasound-guided biopsy. Four radiologists, two experienced and two less-experienced, evaluated each case twice, once without (DL-CAD-) and once assisted by DL-CAD (DL-CAD+). ROC analysis, sensitivities, specificities, PPV and NPV were calculated to compare the diagnostic accuracy for the diagnosis of prostate cancer (PCa) between the two groups (DL-CAD- vs. DL-CAD+). Spearman's correlation coefficients were evaluated to assess the relationship between PI-RADS category and Gleason score (GS). Also, the median reading times were compared for the two reading groups. RESULTS In total, 172 patients were included in the final analysis. With DL-CAD assistance, the overall AUC of the less-experienced radiologists increased significantly from 0.66 to 0.80 (p = 0.001; cutoff ISUP GG ≥ 1) and from 0.68 to 0.80 (p = 0.002; cutoff ISUP GG ≥ 2). Experienced radiologists showed an AUC increase from 0.81 to 0.86 (p = 0.146; cutoff ISUP GG ≥ 1) and from 0.81 to 0.84 (p = 0.433; cutoff ISUP GG ≥ 2). Furthermore, the correlation between PI-RADS category and GS improved significantly in the DL-CAD + group (0.45 vs. 0.57; p = 0.03), while the median reading time was reduced from 157 to 150 s (p = 0.023). CONCLUSIONS DL-CAD assistance increased the mean detection performance, with the most significant benefit for the less-experienced radiologist; with the help of DL-CAD less-experienced radiologists reached performances comparable to that of experienced radiologists. KEY POINTS • DL-CAD used as a concurrent reading aid helps radiologists to distinguish between benign and cancerous lesions in prostate MRI. • With the help of DL-CAD, less-experienced radiologists may achieve detection performances comparable to that of experienced radiologists. • DL-CAD assistance increases the correlation between PI-RADS category and cancer grade.
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Affiliation(s)
- Sandra Labus
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany.
| | - Martin M Altmann
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
| | - Henkjan Huisman
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Angela Tong
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Moon Hyung Choi
- Eunpyeong St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | | | - David J Winkel
- Department of Radiology, University Hospital of Basel, Basel, Switzerland
| | - Pengyi Xing
- Department of Radiology, Changhai Hospital, Shanghai, China
| | | | | | - Robert Grimm
- Diagnostic Imaging, Siemens Healthcare, Erlangen, Germany
| | | | - Ali Kamen
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA
| | - Thomas Herold
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
| | - Clemens Baumann
- Department of Radiology, Helios Klinikum Berlin-Buch, Schwanebecker Ch 50, 13125, Berlin, Germany
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7
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Winkel DJ, Tong A, Lou B, Kamen A, Comaniciu D, Disselhorst JA, Rodríguez-Ruiz A, Huisman H, Szolar D, Shabunin I, Choi MH, Xing P, Penzkofer T, Grimm R, von Busch H, Boll DT. A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study. Invest Radiol 2021; 56:605-613. [PMID: 33787537 DOI: 10.1097/rli.0000000000000780] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. MATERIALS AND METHODS We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. RESULTS The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). CONCLUSIONS Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.
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Affiliation(s)
- David J Winkel
- From the Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
| | - Angela Tong
- Department of Radiology, NYU Langone Health, New York, NY
| | - Bin Lou
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ
| | - Ali Kamen
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ
| | - Dorin Comaniciu
- Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ
| | | | | | - Henkjan Huisman
- Department of Radiology, Radboud University Medical Center, Nijmegen, the Netherlands
| | | | | | - Moon Hyung Choi
- Eunpyeong St Mary's Hospital, Catholic University of Korea, Seoul, Republic of Korea
| | - Pengyi Xing
- Radiology Department, Changhai Hospital of Shanghai, Shanghai, China
| | | | - Robert Grimm
- Siemens Healthineers Diagnostic Imaging, Erlangen, Germany
| | | | - Daniel T Boll
- From the Department of Radiology, University Hospital of Basel, Basel, Basel-Stadt, Switzerland
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Youn SY, Choi MH, Kim DH, Lee YJ, Huisman H, Johnson E, Penzkofer T, Shabunin I, Winkel DJ, Xing P, Szolar D, Grimm R, von Busch H, Son Y, Lou B, Kamen A. Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience. Eur J Radiol 2021; 142:109894. [PMID: 34388625 DOI: 10.1016/j.ejrad.2021.109894] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 06/30/2021] [Accepted: 08/01/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI. METHODS This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test. RESULTS Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4. CONCLUSIONS The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice.
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Affiliation(s)
- Seo Yeon Youn
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Moon Hyung Choi
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Seoul, Republic of Korea.
| | - Dong Hwan Kim
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
| | - Young Joon Lee
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 1021 Seoul, Republic of Korea.
| | - Henkjan Huisman
- Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.
| | - Evan Johnson
- Department of Radiology, New York University, NY, USA
| | - Tobias Penzkofer
- Department of Radiology, Charité, Universitätsmedizin Berlin, Berlin, Germany.
| | | | - David Jean Winkel
- Department of Radiology, University Hospital of Basel, Basel, Switzerland.
| | - Pengyi Xing
- Department of Radiology, Changhai Hospital, Shanghai, China.
| | | | - Robert Grimm
- Diagnostic Imaging, Siemens Healthcare, Erlangen, Germany.
| | | | - Yohan Son
- Siemens Healthineers Ltd., Seoul, Republic of Korea.
| | - Bin Lou
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
| | - Ali Kamen
- Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
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Nael K, Gibson E, Yang C, Ceccaldi P, Yoo Y, Das J, Doshi A, Georgescu B, Janardhanan N, Odry B, Nadar M, Bush M, Re TJ, Huwer S, Josan S, von Busch H, Meyer H, Mendelson D, Drayer BP, Comaniciu D, Fayad ZA. Automated detection of critical findings in multi-parametric brain MRI using a system of 3D neural networks. Sci Rep 2021; 11:6876. [PMID: 33767226 PMCID: PMC7994311 DOI: 10.1038/s41598-021-86022-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 03/08/2021] [Indexed: 01/22/2023] Open
Abstract
With the rapid growth and increasing use of brain MRI, there is an interest in automated image classification to aid human interpretation and improve workflow. We aimed to train a deep convolutional neural network and assess its performance in identifying abnormal brain MRIs and critical intracranial findings including acute infarction, acute hemorrhage and mass effect. A total of 13,215 clinical brain MRI studies were categorized to training (74%), validation (9%), internal testing (8%) and external testing (8%) datasets. Up to eight contrasts were included from each brain MRI and each image volume was reformatted to common resolution to accommodate for differences between scanners. Following reviewing the radiology reports, three neuroradiologists assigned each study to abnormal vs normal, and identified three critical findings including acute infarction, acute hemorrhage, and mass effect. A deep convolutional neural network was constructed by a combination of localization feature extraction (LFE) modules and global classifiers to identify the presence of 4 variables in brain MRIs including abnormal, acute infarction, acute hemorrhage and mass effect. Training, validation and testing sets were randomly defined on a patient basis. Training was performed on 9845 studies using balanced sampling to address class imbalance. Receiver operating characteristic (ROC) analysis was performed. The ROC analysis of our models for 1050 studies within our internal test data showed AUC/sensitivity/specificity of 0.91/83%/86% for normal versus abnormal brain MRI, 0.95/92%/88% for acute infarction, 0.90/89%/81% for acute hemorrhage, and 0.93/93%/85% for mass effect. For 1072 studies within our external test data, it showed AUC/sensitivity/specificity of 0.88/80%/80% for normal versus abnormal brain MRI, 0.97/90%/97% for acute infarction, 0.83/72%/88% for acute hemorrhage, and 0.87/79%/81% for mass effect. Our proposed deep convolutional network can accurately identify abnormal and critical intracranial findings on individual brain MRIs, while addressing the fact that some MR contrasts might not be available in individual studies.
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Affiliation(s)
- Kambiz Nael
- Department of Radiological Sciences, David Geffen School of Medicine at University of California Los Angeles, 757 Westwood Plaza, Suite 1621, Los Angeles, CA, 90095-7532, USA.
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA.
| | - Eli Gibson
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Chen Yang
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Pascal Ceccaldi
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Youngjin Yoo
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Jyotipriya Das
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Amish Doshi
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Bogdan Georgescu
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | | | - Benjamin Odry
- AI for Clinical Analytics, Covera Health, New York, NY, USA
| | - Mariappan Nadar
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Michael Bush
- Magnetic Resonance, Siemens Healthineers, New York, USA
| | - Thomas J Re
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Stefan Huwer
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - Sonal Josan
- Digital Health, Siemens Healthineers, Erlangen, Germany
| | | | - Heiko Meyer
- Magnetic Resonance, Siemens Healthineers, Erlangen, Germany
| | - David Mendelson
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Burton P Drayer
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Dorin Comaniciu
- Digital Technology and Innovation, Siemens Healthineers, Princeton, USA
| | - Zahi A Fayad
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
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Ianhez LE, Busch HV, Arap S, Sabbaga E. [Loss of renal function caused by urinary infection after renal transplantation]. Rev Hosp Clin Fac Med Sao Paulo 1986; 41:11-5. [PMID: 3547588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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11
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Ianhez LE, Busch HV, Sabbaga E, Arap S. [Urinary infection after transplantation: prevalence, predisposing factors and morbidity]. Rev Hosp Clin Fac Med Sao Paulo 1986; 41:16-9. [PMID: 3547589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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