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Jones LI, Marshall A, Elangovan P, Geach R, McKeown-Keegan S, Vinnicombe S, Harding SA, Taylor-Phillips S, Halling-Brown M, Foy C, O'Flynn E, Ghiasvand H, Hulme C, Dunn JA. Evaluating the effectiveness of abbreviated breast MRI (abMRI) interpretation training for mammogram readers: a multi-centre study assessing diagnostic performance, using an enriched dataset. Breast Cancer Res 2022; 24:55. [PMID: 35907862 PMCID: PMC9338668 DOI: 10.1186/s13058-022-01549-5] [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] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 06/30/2022] [Indexed: 11/24/2022]
Abstract
Background Abbreviated breast MRI (abMRI) is being introduced in breast screening trials and clinical practice, particularly for women with dense breasts. Upscaling abMRI provision requires the workforce of mammogram readers to learn to effectively interpret abMRI. The purpose of this study was to examine the diagnostic accuracy of mammogram readers to interpret abMRI after a single day of standardised small-group training and to compare diagnostic performance of mammogram readers experienced in full-protocol breast MRI (fpMRI) interpretation (Group 1) with that of those without fpMRI interpretation experience (Group 2). Methods Mammogram readers were recruited from six NHS Breast Screening Programme sites. Small-group hands-on workstation training was provided, with subsequent prospective, independent, blinded interpretation of an enriched dataset with known outcome. A simplified form of abMRI (first post-contrast subtracted images (FAST MRI), displayed as maximum-intensity projection (MIP) and subtracted slice stack) was used. Per-breast and per-lesion diagnostic accuracy analysis was undertaken, with comparison across groups, and double-reading simulation of a consecutive screening subset. Results 37 readers (Group 1: 17, Group 2: 20) completed the reading task of 125 scans (250 breasts) (total = 9250 reads). Overall sensitivity was 86% (95% confidence interval (CI) 84–87%; 1776/2072) and specificity 86% (95%CI 85–86%; 6140/7178). Group 1 showed significantly higher sensitivity (843/952; 89%; 95%CI 86–91%) and higher specificity (2957/3298; 90%; 95%CI 89–91%) than Group 2 (sensitivity = 83%; 95%CI 81–85% (933/1120) p < 0.0001; specificity = 82%; 95%CI 81–83% (3183/3880) p < 0.0001). Inter-reader agreement was higher for Group 1 (kappa = 0.73; 95%CI 0.68–0.79) than for Group 2 (kappa = 0.51; 95%CI 0.45–0.56). Specificity improved for Group 2, from the first 55 cases (81%) to the remaining 70 (83%) (p = 0.02) but not for Group 1 (90–89% p = 0.44), whereas sensitivity remained consistent for both Group 1 (88–89%) and Group 2 (83–84%). Conclusions Single-day abMRI interpretation training for mammogram readers achieved an overall diagnostic performance within benchmarks published for fpMRI but was insufficient for diagnostic accuracy of mammogram readers new to breast MRI to match that of experienced fpMRI readers. Novice MRI reader performance improved during the reading task, suggesting that additional training could further narrow this performance gap. Supplementary Information The online version contains supplementary material available at 10.1186/s13058-022-01549-5.
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Affiliation(s)
- Lyn I Jones
- North Bristol NHS Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol, BS10 5NB, UK.
| | - Andrea Marshall
- Warwick Clinical Trials Unit, University of Warwick, Coventry, CV4 7AL, UK
| | - Premkumar Elangovan
- Scientific Computing, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK
| | - Rebecca Geach
- North Bristol NHS Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol, BS10 5NB, UK
| | - Sadie McKeown-Keegan
- North Bristol NHS Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol, BS10 5NB, UK
| | - Sarah Vinnicombe
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, GL53 7AS, UK
| | - Sam A Harding
- North Bristol NHS Trust, Southmead Hospital, Southmead Road, Westbury on Trym, Bristol, BS10 5NB, UK
| | | | - Mark Halling-Brown
- Scientific Computing, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK
| | - Christopher Foy
- Research Design Service South West Gloucester Office, National Institute for Health Research (NIHR) Leadon House, Gloucestershire Royal Hospital, Gloucester, GL1 3NN, UK
| | - Elizabeth O'Flynn
- St George's University Hospitals Foundation Trust, London, SW17 0QT, UK
| | - Hesam Ghiasvand
- Institute of Health Research, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Claire Hulme
- Institute of Health Research, University of Exeter Medical School, Exeter, EX1 2LU, UK
| | - Janet A Dunn
- Warwick Clinical Trials Unit, University of Warwick, Coventry, CV4 7AL, UK
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Li Q, Yuan Y, Song G, Liu Y. Nursing Analysis Based on Medical Imaging Technology before and after Coronary Angiography in Cardiovascular Medicine. Appl Bionics Biomech 2022; 2022:3279068. [PMID: 35465185 PMCID: PMC9033406 DOI: 10.1155/2022/3279068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/19/2022] [Accepted: 03/29/2022] [Indexed: 11/17/2022] Open
Abstract
With the advancement of technology, medical imaging technology has been greatly improved. This article mainly studies the nursing before and after coronary angiography in cardiovascular medicine based on medical imaging technology. This paper proposes a multimodal medical image fusion algorithm based on multiscale decomposition and convolution sparse representation. The algorithm first decomposes the preregistered source medical image by NSST, takes the subimages of different scales as training images, and optimizes the subdictionaries of different scales; then convolution and sparse the subimages on each scale encoding to obtain the sparse coefficients of different subimages; secondly, the combination of improved L1 norm and improved spatial frequency (novel sum-modified SF (NMSF)) is used for high-frequency subimage coefficients, and the fusion of low-frequency subimages improved the rule of combining the L1 norm and the regional energy; finally, the final fused image is obtained by inverse NSST of the fused low-frequency subband and high-frequency subband. Experimental analysis found that the bifurcation angle has nothing to do with the damage of the branch vessels after the main branch stent is placed. The bifurcation angle greater than 50° is an independent predictor of MACE after stent extrusion for bifurcation lesions. Experimental results show that the proposed method has good performance in contrast enhancement, detail extraction, and information retention, and it improves the quality of the fusion image.
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Affiliation(s)
- Qin Li
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Yangyang Yuan
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Guangyu Song
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
| | - Yonghua Liu
- Department of Cardiovascular Medicine, Lianyungang First People's Hospital, Lianyungang, 222002 Jiangsu, China
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Abstract
Background Current software applications for human observer studies of images lack flexibility in study design, platform independence, multicenter use, and assessment methods and are not open source, limiting accessibility and expandability. Purpose To develop a user-friendly software platform that enables efficient human observer studies in medical imaging with flexibility of study design. Materials and Methods Software for human observer imaging studies was designed as an open-source web application to facilitate access, platform-independent usability, and multicenter studies. Different interfaces for study creation, participation, and management of results were implemented. The software was evaluated in human observer experiments between May 2019 and March 2021, in which duration of observer responses was tracked. Fourteen radiologists evaluated and graded software usability using the 100-point system usability scale. The application was tested in Chrome, Firefox, Safari, and Edge browsers. Results Software function was designed to allow visual grading analysis (VGA), multiple-alternative forced-choice (m-AFC), receiver operating characteristic (ROC), localization ROC, free-response ROC, and customized designs. The mean duration of reader responses per image or per image set was 6.2 seconds ± 4.8 (standard deviation), 5.8 seconds ± 4.7, 8.7 seconds ± 5.7, and 6.0 seconds ± 4.5 in four-AFC with 160 image quartets per reader, four-AFC with 640 image quartets per reader, localization ROC, and experimental studies, respectively. The mean system usability scale score was 83 ± 11 (out of 100). The documented code and a demonstration of the application are available online (https://github.com/genskeu/HON, https://hondemo.pythonanywhere.com/). Conclusion A user-friendly and efficient open-source application was developed for human reader experiments that enables study design versatility, as well as platform-independent and multicenter usability. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Thompson in this issue.
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Affiliation(s)
- Ulrich Genske
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (U.G., P.J.); Data Analytics and Computational Statistics, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany (U.G.); and Berlin Institute of Health, Berlin, Germany (P.J.)
| | - Paul Jahnke
- From the Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117 Berlin, Germany (U.G., P.J.); Data Analytics and Computational Statistics, Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany (U.G.); and Berlin Institute of Health, Berlin, Germany (P.J.)
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Mackenzie A, Thomson EL, Mitchell M, Elangovan P, van Ongeval C, Cockmartin L, Warren LM, Wilkinson LS, Wallis MG, Given-Wilson RM, Dance DR, Young KC. Virtual clinical trial to compare cancer detection using combinations of 2D mammography, digital breast tomosynthesis and synthetic 2D imaging. Eur Radiol 2022; 32:806-814. [PMID: 34331118 DOI: 10.1007/s00330-021-08197-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 03/18/2021] [Revised: 06/07/2021] [Accepted: 07/01/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVES This study was designed to compare the detection of subtle lesions (calcification clusters or masses) when using the combination of digital breast tomosynthesis (DBT) and synthetic mammography (SM) with digital mammography (DM) alone or combined with DBT. METHODS A set of 166 cases without cancer was acquired on a DBT mammography system. Realistic subtle calcification clusters and masses in the DM images and DBT planes were digitally inserted into 104 of the acquired cases. Three study arms were created: DM alone, DM with DBT and SM with DBT. Five mammographic readers located the centre of any lesion within the images that should be recalled for further investigation and graded their suspiciousness. A JAFROC figure of merit (FoM) and lesion detection fraction (LDF) were calculated for each study arm. The visibility of the lesions in the DBT images was compared with SM and DM images. RESULTS For calcification clusters, there were no significant differences (p > 0.075) in FoM or LDF. For masses, the FoM and LDF were significantly improved in the arms using DBT compared to DM alone (p < 0.001). On average, both calcification clusters and masses were more visible on DBT than on DM and SM images. CONCLUSIONS This study demonstrated that masses were detected better with DBT than with DM alone and there was no significant difference (p = 0.075) in LDF between DM&DBT and SM&DBT for calcifications clusters. Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses. KEY POINTS • The detection of masses was significantly better using DBT than with digital mammography alone. • The detection of calcification clusters was not significantly different between digital mammography and synthetic 2D images combined with tomosynthesis. • Our results support previous studies that it may be acceptable to not acquire digital mammography alongside tomosynthesis for subtle calcification clusters and ill-defined masses for the imaging technology used.
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Affiliation(s)
- Alistair Mackenzie
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK.
| | - Emma L Thomson
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Melissa Mitchell
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Premkumar Elangovan
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
| | | | - Lesley Cockmartin
- Department of Imaging and Pathology, Division of Medical Physics and Quality Assessment, KU Leuven, Herestraat 49, B-3000, Leuven, Belgium
| | - Lucy M Warren
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
| | - Louise S Wilkinson
- Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Matthew G Wallis
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge & NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | | | - David R Dance
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
| | - Kenneth C Young
- National Coordinating Centre for the Physics in Mammography, Royal Surrey NHS Foundation Trust, Guildford, UK
- Department of Physics, University of Surrey, Guildford, UK
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Burnside ES, Warren LM, Myles J, Wilkinson LS, Wallis MG, Patel M, Smith RA, Young KC, Massat NJ, Duffy SW. Quantitative breast density analysis to predict interval and node-positive cancers in pursuit of improved screening protocols: a case-control study. Br J Cancer 2021; 125:884-892. [PMID: 34168297 PMCID: PMC8438060 DOI: 10.1038/s41416-021-01466-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [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: 11/07/2020] [Revised: 05/18/2021] [Accepted: 06/10/2021] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND This study investigates whether quantitative breast density (BD) serves as an imaging biomarker for more intensive breast cancer screening by predicting interval, and node-positive cancers. METHODS This case-control study of 1204 women aged 47-73 includes 599 cancer cases (302 screen-detected, 297 interval; 239 node-positive, 360 node-negative) and 605 controls. Automated BD software calculated fibroglandular volume (FGV), volumetric breast density (VBD) and density grade (DG). A radiologist assessed BD using a visual analogue scale (VAS) from 0 to 100. Logistic regression and area under the receiver operating characteristic curves (AUC) determined whether BD could predict mode of detection (screen-detected or interval); node-negative cancers; node-positive cancers, and all cancers vs. controls. RESULTS FGV, VBD, VAS, and DG all discriminated interval cancers (all p < 0.01) from controls. Only FGV-quartile discriminated screen-detected cancers (p < 0.01). Based on AUC, FGV discriminated all cancer types better than VBD or VAS. FGV showed a significantly greater discrimination of interval cancers, AUC = 0.65, than of screen-detected cancers, AUC = 0.61 (p < 0.01) as did VBD (0.63 and 0.53, respectively, p < 0.001). CONCLUSION FGV, VBD, VAS and DG discriminate interval cancers from controls, reflecting some masking risk. Only FGV discriminates screen-detected cancers perhaps adding a unique component of breast cancer risk.
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Affiliation(s)
- Elizabeth S Burnside
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, E3/311 Clinical Science Center, Madison, WI, USA.
| | - Lucy M Warren
- National Co-ordinating Centre for the Physics of Mammography (NCCPM), Medical Physics Department, Royal Surrey County Hospital, Guildford, UK
| | - Jonathan Myles
- Centre for Cancer Prevention, Queen Mary University of London, Wolfson Institute of Preventive Medicine, London, UK
| | | | - Matthew G Wallis
- Cambridge Breast Unit and NIHR Cambridge Biomedical Research Centre, Cambridge University Hospitals NHS Trust, Cambridge, UK
| | - Mishal Patel
- Scientific Computing, Medical Physics Department, Royal Surrey County Hospital, Guildford, UK
| | | | - Kenneth C Young
- National Co-ordinating Centre for the Physics of Mammography (NCCPM), Medical Physics Department, Royal Surrey County Hospital, Guildford, UK
| | - Nathalie J Massat
- Centre for Cancer Prevention, Queen Mary University of London, Wolfson Institute of Preventive Medicine, London, UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Queen Mary University of London, Wolfson Institute of Preventive Medicine, London, UK
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Halling-Brown MD, Warren LM, Ward D, Lewis E, Mackenzie A, Wallis MG, Wilkinson LS, Given-Wilson RM, McAvinchey R, Young KC. OPTIMAM Mammography Image Database: A Large-Scale Resource of Mammography Images and Clinical Data. Radiol Artif Intell 2021; 3:e200103. [PMID: 33937853 PMCID: PMC8082293 DOI: 10.1148/ryai.2020200103] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/03/2020] [Accepted: 10/05/2020] [Indexed: 11/11/2022]
Abstract
Supplemental material is available for this article.
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Affiliation(s)
- Mark D. Halling-Brown
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Lucy M. Warren
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Dominic Ward
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Emma Lewis
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Alistair Mackenzie
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Matthew G. Wallis
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Louise S. Wilkinson
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Rosalind M. Given-Wilson
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Rita McAvinchey
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
| | - Kenneth C. Young
- From the Department of Scientific Computing (M.D.H.B., D.W., E.L.) and National Co-ordinating Centre for the Physics of Mammography (L.M.W., A.M., K.C.Y.), Royal Surrey NHS Foundation Trust, Egerton Road, Guildford GU2 7XX, England; Centre for Vision, Speech and Signal Processing (M.D.H.B., E.L.) and Department of Physics (K.C.Y.), University of Surrey, Guildford, England; Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, England (M.G.W.); NIHR Cambridge Biomedical Research Centre, Cambridge, England (M.G.W.); Oxford Breast Imaging Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, England (L.S.W.); Department of Radiology, St George’s Healthcare NHS Trust, London, England (R.M.G.W.); and Jarvis Breast Screening Centre, Guildford, England (R.M.)
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Min Q, Wang X, Huang B, Xu L. Web-Based Technology for Remote Viewing of Radiological Images: App Validation. J Med Internet Res 2020; 22:e16224. [PMID: 32975520 PMCID: PMC7547396 DOI: 10.2196/16224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 09/18/2019] [Revised: 07/21/2020] [Accepted: 08/11/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Internet technologies can create advanced and rich web-based apps that allow radiologists to easily access teleradiology systems and remotely view medical images. However, each technology has its own drawbacks. It is difficult to balance the advantages and disadvantages of these internet technologies and identify an optimal solution for the development of medical imaging apps. OBJECTIVE This study aimed to compare different internet platform technologies for remotely viewing radiological images and analyze their advantages and disadvantages. METHODS Oracle Java, Adobe Flash, and HTML5 were each used to develop a comprehensive web-based medical imaging app that connected to a medical image server and provided several required functions for radiological interpretation (eg, navigation, magnification, windowing, and fly-through). Java-, Flash-, and HTML5-based medical imaging apps were tested on different operating systems over a local area network and a wide area network. Three computed tomography colonography data sets and 2 ordinary personal computers were used in the experiment. RESULTS The experimental results demonstrated that Java-, Flash-, and HTML5-based apps had the ability to provide real-time 2D functions. However, for 3D, performances differed between the 3 apps. The Java-based app had the highest frame rate of volume rendering. However, it required the longest time for surface rendering and failed to run surface rendering in macOS. The HTML5-based app had the fastest surface rendering and the highest speed for fly-through without platform dependence. Volume rendering, surface rendering, and fly-through performances of the Flash-based app were significantly worse than those of the other 2 apps. CONCLUSIONS Oracle Java, Adobe Flash, and HTML5 have individual strengths in the development of remote access medical imaging apps. However, HTML5 is a promising technology for remote viewing of radiological images and can provide excellent performance without requiring any plug-ins.
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Affiliation(s)
- Qiusha Min
- School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China
| | - Xin Wang
- School of Educational Information Technology, Central China Normal University, Wuhan, Hubei, China
| | - Bo Huang
- Department of Radiology, Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China
| | - Liangzhou Xu
- Department of Radiology, Wuhan Hospital of Traditional Chinese Medicine, Wuhan, China
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Precht H, Hansson J, Outzen C, Hogg P, Tingberg A. Radiographers' perspectives' on Visual Grading Analysis as a scientific method to evaluate image quality. Radiography (Lond) 2019; 25 Suppl 1:S14-S18. [PMID: 31481182 DOI: 10.1016/j.radi.2019.06.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [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: 05/10/2019] [Revised: 06/25/2019] [Accepted: 06/26/2019] [Indexed: 12/15/2022]
Abstract
INTRODUCTION Radiographers routinely undertake many initiatives to balance image quality with radiation dose (optimisation). For optimisation studies to be successful image quality needs to be carefully evaluated. Purpose was to 1) discuss the strengths and limitations of a Visual Grading Analysis (VGA) method for image quality evaluation and 2) to outline the method from a radiographer's perspective. METHODS A possible method for investigating and discussing the relationship between radiographic image quality parameters and the interpretation and perception of X-ray images is the VGA method. VGA has a number of advantages such as being low cost and a detailed image quality assessment, although it is limited to ensure the images convey the relevant clinical information and relate the task based radiography. RESULTS Comparing the experience of using VGA and Receiver Operating Characteristic (ROC) it is obviously that less papers are published on VGA (Pubmed n=1.384) compared to ROC (Pubmed n=122.686). Hereby the scientific experience of the VGA method is limited compared to the use of ROC. VGA is, however, a much newer method and it is slowly gaining more and more attention. CONCLUSION The success of VGA requires a number of steps to be completed, such as defining the VGA criteria, choosing the VGA method (absolute or relative), including observers, finding the best image display platforms, training observers and selecting the best statistical method for the study purpose should be thoroughly considered. IMPLICATION FOR PRACTICE Detailed evaluation of image quality for optimisation studies related to technical definition of image quality.
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Affiliation(s)
- H Precht
- Conrad Research Programme, University College Lillebelt, Niels Bohrs Alle 1, 5230, Odense M, Denmark; Medical Research Department, Odense University Hospital, Baagøes Àlle 15, 5700, Svendborg, Denmark; Department of Clinical Research, University of Southern Denmark, Winsløwsparken, 5000, Odense C, Denmark.
| | - J Hansson
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, SE-413 45, Gothenburg, Sweden; Department of Radiation Physics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, SE-413 45, Gothenburg, Sweden
| | - C Outzen
- Conrad Research Programme, University College Lillebelt, Niels Bohrs Alle 1, 5230, Odense M, Denmark
| | - P Hogg
- School of Health and Society, University of Salford, Manchester, UK
| | - A Tingberg
- Medical Radiation Physics, Department of Clinical Sciences, Lund University, Sweden; Skåne University Hospital, 205 02, Malmö, Sweden
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Min Q, Wang Z, Liu N. An Evaluation of HTML5 and WebGL for Medical Imaging Applications. J Healthc Eng 2018; 2018:1592821. [PMID: 30245782 DOI: 10.1155/2018/1592821] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/22/2018] [Accepted: 07/18/2018] [Indexed: 11/21/2022]
Abstract
Despite the fact that a large number of web applications are used in the medical community, there are still certain technological challenges that need to be addressed, for example, browser plug-ins and efficient 3D visualization. These problems make it necessary for a specific browser plug-in to be preinstalled on the client side when launching applications. Otherwise, the applications fail to run due to the lack of the required software. This paper presents the latest techniques in hypertext markup language 5 (HTML5) and web graphics library (WebGL) for solving these problems and an evaluation of the suitability of the combination of HTML5 and WebGL for the development of web-based medical imaging applications. In this study, a comprehensive medical imaging application was developed using HTML5 and WebGL. This application connects to the medical image server, runs on a standard personal computer (PC), and is easily accessible via a standard web browser. The several functions required for radiological interpretation were implemented, for example, navigation, magnification, windowing, and fly-through. The HTML5-based medical imaging application was tested on major browsers and different operating systems over a local area network (LAN) and a wide area network (WAN). The experimental results revealed that this application successfully performed two-dimensional (2D) and three-dimensional (3D) functions on different PCs over the LAN and WAN. Moreover, it demonstrated an excellent performance for remote access users, especially over a short time period for 3D visualization and a real-time fly-through navigation. The results of the study demonstrate that HTML5 and WebGL combination is suitable for the development of medical imaging applications. Moreover, the advantages and limitations of these technologies are discussed in this paper.
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Kostopoulos S, Ravazoula P, Asvestas P, Kalatzis I, Xenogiannopoulos G, Cavouras D, Glotsos D. Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research. J Digit Imaging 2018; 30:287-295. [PMID: 28083826 DOI: 10.1007/s10278-017-9947-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
Histopathology image processing, analysis and computer-aided diagnosis have been shown as effective assisting tools towards reliable and intra-/inter-observer invariant decisions in traditional pathology. Especially for cancer patients, decisions need to be as accurate as possible in order to increase the probability of optimal treatment planning. In this study, we propose a new image collection library (HICL-Histology Image Collection Library) comprising 3831 histological images of three different diseases, for fostering research in histopathology image processing, analysis and computer-aided diagnosis. Raw data comprised 93, 116 and 55 cases of brain, breast and laryngeal cancer respectively collected from the archives of the University Hospital of Patras, Greece. The 3831 images were generated from the most representative regions of the pathology, specified by an experienced histopathologist. The HICL Image Collection is free for access under an academic license at http://medisp.bme.teiath.gr/hicl/ . Potential exploitations of the proposed library may span over a board spectrum, such as in image processing to improve visualization, in segmentation for nuclei detection, in decision support systems for second opinion consultations, in statistical analysis for investigation of potential correlations between clinical annotations and imaging findings and, generally, in fostering research on histopathology image processing and analysis. To the best of our knowledge, the HICL constitutes the first attempt towards creation of a reference image collection library in the field of traditional histopathology, publicly and freely available to the scientific community.
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Affiliation(s)
- Spiros Kostopoulos
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece
| | - Panagiota Ravazoula
- Department of Pathology, University Hospital of Patras, Rio, 265 04, Patras, Greece
| | - Pantelis Asvestas
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece
| | - Ioannis Kalatzis
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece
| | - George Xenogiannopoulos
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece
| | - Dionisis Cavouras
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece
| | - Dimitris Glotsos
- Medical Image and Signal Processing Laboratory (MEDISP), Department of Biomedical Engineering, Technological Educational Institute of Athens, Ag. Spyridonos Street, 122 10, Egaleo, Athens, Greece.
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Warren LM, Halling-Brown MD, Looney PT, Dance DR, Wallis MG, Given-Wilson RM, Wilkinson L, McAvinchey R, Young KC. Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images. Clin Radiol 2017; 72:799.e1-799.e8. [PMID: 28457521 DOI: 10.1016/j.crad.2017.03.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 01/24/2017] [Accepted: 03/23/2017] [Indexed: 11/19/2022]
Abstract
AIM To investigate the effect of image processing on cancer detection in mammography. METHODS AND MATERIALS An observer study was performed using 349 digital mammography images of women with normal breasts, calcification clusters, or soft-tissue lesions including 191 subtle cancers. Images underwent two types of processing: FlavourA (standard) and FlavourB (added enhancement). Six observers located features in the breast they suspected to be cancerous (4,188 observations). Data were analysed using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis. Characteristics of the cancers detected with each image processing type were investigated. RESULTS For calcifications, the JAFROC figure of merit (FOM) was equal to 0.86 for both types of image processing. For soft-tissue lesions, the JAFROC FOM were better for FlavourA (0.81) than FlavourB (0.78); this difference was significant (p=0.001). Using FlavourA a greater number of cancers of all grades and sizes were detected than with FlavourB. FlavourA improved soft-tissue lesion detection in denser breasts (p=0.04 when volumetric density was over 7.5%) CONCLUSIONS: The detection of malignant soft-tissue lesions (which were primarily invasive) was significantly better with FlavourA than FlavourB image processing. This is despite FlavourB having a higher contrast appearance often preferred by radiologists. It is important that clinical choice of image processing is based on objective measures.
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Affiliation(s)
- L M Warren
- National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK.
| | - M D Halling-Brown
- Scientific Computing, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK
| | - P T Looney
- National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK
| | - D R Dance
- National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK; Department of Physics, University of Surrey, Guildford, Surrey, GU2 7JP, UK
| | - M G Wallis
- Cambridge Breast Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK; NIHR Cambridge Biomedical Research Centre, Cambridge, CB2 0QQ, UK
| | - R M Given-Wilson
- Department of Radiology, St George's University Hospitals NHS Foundation Trust, Tooting, London, SW17 0QT, UK
| | - L Wilkinson
- Department of Radiology, St George's University Hospitals NHS Foundation Trust, Tooting, London, SW17 0QT, UK
| | - R McAvinchey
- Jarvis Breast Screening and Diagnostic Centre, Guildford, GU1 1LJ, UK
| | - K C Young
- National Co-ordinating Centre for the Physics of Mammography, Royal Surrey County Hospital NHS Foundation Trust, Guildford, GU2 7XX, UK; Department of Physics, University of Surrey, Guildford, Surrey, GU2 7JP, UK
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Abstract
ViewDEX (Viewer for Digital Evaluation of X-ray images) is an image viewer and task manager suitable for research and optimisation tasks in medical imaging. The software has undergone continuous development during more than a decade and has during this time period been used in numerous studies. ViewDEX is DICOM compatible, and the features of the interface (tasks, image handling and functionality) are general and flexible. The set-up of a study is determined by altering properties in a text-editable file, enabling easy and flexible configuration. ViewDEX is developed in Java and can run from any disc area connected to a computer. It is free to use for non-commercial purposes and can be downloaded from http://www.vgregion.se/sas/viewdex The purposes of the present article are to give a short overview of the development of ViewDEX and to describe recent updates of the software. In addition, a description on how to configure a viewing session in ViewDEX is provided.
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Affiliation(s)
- Angelica Svalkvist
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden Department of Radiation Physics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, SE-413 45 Gothenburg, Sweden
| | - Sune Svensson
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden
| | - Markus Håkansson
- Department of Radiation Physics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, SE-413 45 Gothenburg, Sweden Department of Diagnostic Radiology, Södra Älvsborgs sjukhus, SE-501 82 Borås, Sweden
| | - Magnus Båth
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden Department of Radiation Physics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, SE-413 45 Gothenburg, Sweden
| | - Lars Gunnar Månsson
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, SE-413 45 Gothenburg, Sweden Department of Radiation Physics, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, SE-413 45 Gothenburg, Sweden
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