In European journal of cancer (Oxford, England : 1990)
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.
Pfob André, Sidey-Gibbons Chris, Barr Richard G, Duda Volker, Alwafai Zaher, Balleyguier Corinne, Clevert Dirk-André, Fastner Sarah, Gomez Christina, Goncalo Manuela, Gruber Ines, Hahn Markus, Hennigs André, Kapetas Panagiotis, Lu Sheng-Chieh, Nees Juliane, Ohlinger Ralf, Riedel Fabian, Rutten Matthieu, Schaefgen Benedikt, Stieber Anne, Togawa Riku, Tozaki Mitsuhiro, Wojcinski Sebastian, Xu Cai, Rauch Geraldine, Heil Joerg, Golatta Michael
2022-Sep-28
Artificial intelligence, Breast cancer, Breast imaging, Elastography, Machine learning
