Artificial intelligence for ultrasound microflow imaging in breast cancer diagnosis

Abstract

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.

Zusammenfassung

Ziel Entwicklung und Bewertung von Algorithmen der künstlichen Intelligenz (KI) für das Ultraschall-Microflow-Imaging (MFI) in der Brustkrebsdiagnose.

Material und Methoden Wir sammelten retrospektiv einen Datensatz, der aus 516 Brustläsionen (364 benigne und 152 maligne) von 471 Frauen bestand, die sich dem B-Mode-US und dem MFI unterzogen. Der interne Datensatz wurde in einen Trainings- (n=410) und einen Testdatensatz (n=106) aufgeteilt, um KI-Algorithmen auf der Grundlage tiefer Convolutional-Neural-Networks aus MFI zu entwickeln. Die KI-Algorithmen wurden trainiert, um das Malignitätsrisiko (0–100%) zu ermitteln. Die entwickelten KI-Algorithmen wurden mit einem unabhängigen externen Datensatz von 264 Läsionen (229 benignen und 35 malignen) weiter validiert. Die diagnostische Leistung von B-Mode-US, KI-Algorithmen oder deren Kombinationen wurde durch Berechnung der Fläche unter der Receiver-Operating-Characteristic-Curve (AUROC) bewertet.

Ergebnisse Die AUROC der 3 entwickelten KI-Algorithmen (0,955–0,966) war höher als die des B-Mode-US (0,842; p<0,0001). Die AUROC der KI-Algorithmen im externen Validierungsdatensatz (0,892–0,920) war ähnlich wie die des Testdatensatzes. Unter den KI-Algorithmen wurde kein signifikanter Unterschied in allen Leistungsmetriken in Kombination mit oder ohne B-Mode-US gefunden. Die Kombination aus B-Mode-US und KI-Algorithmen wies einen höheren AUROC-Wert (0,963–0,972) auf als der B-Mode-US (p<0,0001). Durch die Kombination von B-Mode-US und KI-Algorithmen konnte die falsch-positive Rate von Läsionen der BI-RADS-Kategorie 4A signifikant von 87% auf 13% gesenkt werden (p<0,0001).

Schlussfolgerung Das KI-basierte MFI diagnostizierte Brustkrebs mit besserer Leistung als der B-Mode-US und eliminierte 74% der falsch-positiven Diagnosen bei Läsionen der BI-RADS-Kategorie 4A.

Keywords

artificial intelligence - deep learning - microvascular blood flow - breast cancer - ultrasonography, Doppler

Publication History

Received: 15 June 2023

Accepted after revision: 06 December 2023

Article published online:
09 April 2024

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