Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis.

Autor:	Eun NL; Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)., Lee E; Computational Science and Engineering, Yonsei University, Seoul, Korea (the Republic of)., Park AY; Radiology, Bundang CHA Medical Center, Seongnam, Korea (the Republic of)., Son EJ; Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)., Kim JA; Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of)., Youk JH; Department of Radiology, Yonsei University College of Medicine, Seoul, Korea, Republic of.; Radiology, Gangnam Severance Hospital, Seoul, Korea (the Republic of).
Jazyk:	angličtina
Zdroj:	Ultraschall in der Medizin (Stuttgart, Germany : 1980) [Ultraschall Med] 2024 Aug; Vol. 45 (4), pp. 412-417. Date of Electronic Publication: 2024 Apr 09.
DOI:	10.1055/a-2230-2455
Abstrakt:	Purpose: To develop and evaluate artificial intelligence (AI) algorithms for ultrasound (US) microflow imaging (MFI) in breast cancer diagnosis. Materials and Methods: We retrospectively collected a dataset consisting of 516 breast lesions (364 benign and 152 malignant) in 471 women who underwent B-mode US and MFI. The internal dataset was split into training (n = 410) and test datasets (n = 106) for developing AI algorithms from deep convolutional neural networks from MFI. AI algorithms were trained to provide malignancy risk (0-100%). The developed AI algorithms were further validated with an independent external dataset of 264 lesions (229 benign and 35 malignant). The diagnostic performance of B-mode US, AI algorithms, or their combinations was evaluated by calculating the area under the receiver operating characteristic curve (AUROC). Results: The AUROC of the developed three AI algorithms (0.955-0.966) was higher than that of B-mode US (0.842, P < 0.0001). The AUROC of the AI algorithms on the external validation dataset (0.892-0.920) was similar to that of the test dataset. Among the AI algorithms, no significant difference was found in all performance metrics combined with or without B-mode US. Combined B-mode US and AI algorithms had a higher AUROC (0.963-0.972) than that of B-mode US (P < 0.0001). Combining B-mode US and AI algorithms significantly decreased the false-positive rate of BI-RADS category 4A lesions from 87% to 13% (P < 0.0001). Conclusion: AI-based MFI diagnosed breast cancers with better performance than B-mode US, eliminating 74% of false-positive diagnoses in BI-RADS category 4A lesions. Competing Interests: The authors declare that they have no conflict of interest. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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