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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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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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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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Goncharuk D, Veliev E, Sokolov E, Shabunin I, Paklina O, Setdikova G, Semiletov N. Predictive power of non-invasive mp-MRI markers for clinically significant prostate cancer. EUR UROL SUPPL 2020. [DOI: 10.1016/s2666-1683(20)32889-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Panov V, Shipanova I, Michtchenko A, Shabunin I, Shimanovskii N, Sibeldina L, Sergeev P. 1H and 13C NMR study of the molecular interaction mechanism between chloramphenicol and human serum albumin. Biochem Mol Biol Int 1995; 35:457-60. [PMID: 7663402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Molecular mechanism of the interaction between human serum albumin and cloramphenicol was studied by 1H and 13C NMR-spectroscopy. It was found that the main role belongs to [formula: see text] groups of chloramphenicol. A schematic model of the complex-formation between serum albumin and chloramphenicol was proposed.
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Affiliation(s)
- V Panov
- Institute of Chemical Physics RAS, Moscow, Russia
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Panov V, Egorova S, Vetrov O, Moscalev D, Gladky A, Shabunin I, Bolotova E, Popov K, Akhadov T, Shimanovsky N, Sergeev P. Development of the MRI contrast media: New original chelates Gd-complexes. Pharmacol Res 1995. [DOI: 10.1016/1043-6618(95)87526-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Panov V, Vetrov O, Shabunin I, Avdienko V, Egorova S, Lyashenko S, Kondrashov S, Akhadov T, Shimanovsky N, Sergeev P. The influence of new nonionic roentgen contrast media on the human blood plasma gamma-globulins. Pharmacol Res 1995. [DOI: 10.1016/1043-6618(95)87770-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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