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| Verfasst von: | Pfob, André [VerfasserIn]  |
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| | Sidey-Gibbons, Chris [VerfasserIn] |
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| | Barr, Richard G. [VerfasserIn] |
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| | Duda, Volker [VerfasserIn] |
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| | Alwafai, Zaher [VerfasserIn] |
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| | Balleyguier, Corinne [VerfasserIn] |
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| | Clevert, Dirk-André [VerfasserIn] |
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| | Fastner, Sarah [VerfasserIn] |
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| | Gomez, Christina [VerfasserIn] |
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| | Goncalo, Manuela [VerfasserIn] |
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| | Gruber, Ines [VerfasserIn] |
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| | Hahn, Markus [VerfasserIn] |
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| | Hennigs, André [VerfasserIn] |
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| | Kapetas, Panagiotis [VerfasserIn] |
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| | Lu, Sheng-Chieh [VerfasserIn] |
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| | Nees, Juliane [VerfasserIn] |
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| | Ohlinger, Ralf [VerfasserIn] |
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| | Riedel, Fabian [VerfasserIn] |
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| | Rutten, Matthieu [VerfasserIn] |
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| | Schäfgen, Benedikt [VerfasserIn] |
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| | Stieber, Anne [VerfasserIn] |
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| | Togawa, Riku [VerfasserIn] |
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| | Tozaki, Mitsuhiro [VerfasserIn] |
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| | Wojcinski, Sebastian [VerfasserIn] |
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| | Xu, Cai [VerfasserIn] |
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| | Rauch, Geraldine [VerfasserIn] |
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| | Heil, Jörg [VerfasserIn] |
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| | Golatta, Michael [VerfasserIn] |
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| Titel: | Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002) |
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| Titelzusatz: | a retrospective, international, multicentre analysis |
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| Verf.angabe: | André Pfob, Chris Sidey-Gibbons, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Joerg Heil, Michael Golatta |
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| E-Jahr: | 2022 |
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| Jahr: | 28 September 2022 |
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| Umfang: | 14 S. |
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| Fussnoten: | Gesehen am 06.02.2023 ; An abstract reporting final results was presented as Spotlight presentation at the San Antonio Breast Cancer Symposium 2021 on December 9th, 5:00:00 AM - 6:30:00 PM, program number PD11-05 |
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| Titel Quelle: | Enthalten in: European journal of cancer |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier, 1992 |
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| Jahr Quelle: | 2022 |
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| Band/Heft Quelle: | 177(2022) vom: Dez., Seite 1-14 |
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| ISSN Quelle: | 1879-0852 |
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| Abstract: | Background - Breast ultrasound identifies additional carcinomas not detected in mammography but has a higher rate of false-positive findings. We evaluated whether use of intelligent multi-modal shear wave elastography (SWE) can reduce the number of unnecessary biopsies without impairing the breast cancer detection rate. - Methods - We trained, tested, and validated machine learning algorithms using SWE, clinical, and patient information to classify breast masses. We used data from 857 women who underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation at 12 study sites in seven countries from 2016 to 2019. Algorithms were trained and tested on data from 11 of the 12 sites and externally validated using the additional site's data. We compared findings to the histopathologic evaluation and compared the diagnostic performance between B-mode breast ultrasound, traditional SWE, and intelligent multi-modal SWE. - Results - In the external validation set (n = 285), intelligent multi-modal SWE showed a sensitivity of 100% (95% CI, 97.1-100%, 126 of 126), a specificity of 50.3% (95% CI, 42.3-58.3%, 80 of 159), and an area under the curve of 0.93 (95% CI, 0.90-0.96). Diagnostic performance was significantly higher compared to traditional SWE and B-mode breast ultrasound (P < 0.001). Unlike traditional SWE, positive-predictive values of intelligent multi-modal SWE were significantly higher compared to B-mode breast ultrasound. Unnecessary biopsies were reduced by 50.3% (79 versus 159, P < 0.001) without missing cancer compared to B-mode ultrasound. - Conclusion - The majority of unnecessary breast biopsies might be safely avoided by using intelligent multi-modal SWE. These results may be helpful to reduce diagnostic burden for patients, providers, and healthcare systems. |
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| DOI: | doi:10.1016/j.ejca.2022.09.018 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.ejca.2022.09.018 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S0959804922007584 |
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| | DOI: https://doi.org/10.1016/j.ejca.2022.09.018 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Breast cancer |
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| | Breast imaging |
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| | Elastography |
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| | Machine learning |
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| K10plus-PPN: | 1833265734 |
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| Verknüpfungen: | → Zeitschrift |
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Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002) / Pfob, André [VerfasserIn]; 28 September 2022 (Online-Ressource)
