Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): A retrospective, international, multicentre analysis
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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Two further studies resulted in the possibility of avoiding unnecessary breast biopsies.
“The possibility of avoiding unnecessary breast biopsies was the result of two further studies”
One of the two studies on avoiding breast biopsies used an AI-assisted multi-modal shear wave elastography model.
“of which one used an AI-assisted multi-modal shear wave elastography model [199,200].”
The use of AI-assisted US has been shown to reduce unnecessary breast biopsies.
“the use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis and unnecessary breast biopsies [197,199,200].”
The use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis.
“the use of AI-assisted US has been shown to reduce the amount of unnecessary hospital admissions due to misdiagnosis and unnecessary breast biopsies [197,199,200].”
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“The possibility of avoiding unnecessary breast biopsies was the result of two further studies, of which one used an AI-assisted multi-modal shear wave elastography model [199,200].”
BackgroundResult
- Authors
- Pfob, A., Sidey-Gibbons, C., Barr, R. G., Duda, V., Alwafai, Z., Balleyguier, C., Clevert, D.-A., Fastner, S., Gomez, C., Goncalo, M., Gruber, I., Hahn, M., Hennigs, A., Kapetas, P., Lu, S.-C., Nees, J., Ohlinger, R., Riedel, F., Rutten, M., Schaefgen, B., Stieber, A., Togawa, R., Tozaki, M., Wojcinski, S., Xu, C., Rauch, G., Heil, J., & Golatta, M.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- Northeast Ohio Medical University (US)
- Philipps University of Marburg (DE)
- Universitätsmedizin Greifswald (DE)
- Institut Gustave Roussy (FR)
- Ludwig-Maximilians-Universität München (DE)
- Heidelberg University (DE)
- University of Tübingen (DE)
- Medical University of Vienna (AT)
- Radboud University Medical Center (NL)
- Sagara Hospital (JP)
- Klinikum Bielefeld (DE)
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Golatta, Michael
- ORCID
- Pfob, André
- Sidey-Gibbons, Chris
- Barr, Richard G.
- Alwafai, Zaher
- Balleyguier, Corinne
- Clevert, Dirk-André
- Fastner, Sarah
- Gomez, Christina
- Gruber, Ines
- Hahn, Markus
- Hennigs, André
- Kapetas, Panagiotis
- Lu, Sheng-Chieh
- Nees, Juliane
- Riedel, Fabian
- Rutten, Matthieu
- Schaefgen, Benedikt
- Tozaki, Mitsuhiro
- Wojcinski, Sebastian
- Xu, Cai
- Rauch, Geraldine
- Heil, Joerg
- Golatta, Michael
- Venue
- European Journal of Cancer
- Publisher
- Elsevier BV
- Published
- 2022-12
- Volume
- 177
- Pages
- 1-14
- ISSN
- 0959-8049 (ISSN-L)
- OpenAlex
- W4297537237
- Cited by
- 27
- References
- 44
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 4 · 2023: 5
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- European Journal of Cancer (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Siemens Medical Solutions USA
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Pathology and Forensic Medicine › Breast Lesions and Carcinomas
- Keywords
- breast cancer, shear wave elastography, breast ultrasound, breast mass classification, machine learning, diagnostic accuracy, false positives
- MeSH terms
- Biopsy, Breast Neoplasms, Diagnosis, Differential, Female, Humans, Retrospective Studies, Sensitivity and Specificity, Reproducibility of Results, Ultrasonography, Mammary, Elasticity Imaging Techniques
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