REVIEW article
Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1578991
This article is part of the Research TopicAdvancing Breast Cancer Care Through Transparent AI and Federated Learning: Integrating Radiological, Histopathological, and Clinical Data for Diagnosis, Recurrence Prediction, and SurvivorshipView all 4 articles
Artificial intelligence-based automated breast ultrasound radiomics for breast tumor diagnosis and treatment: a narrative review
Provisionally accepted- 1Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan Province, China
- 2First People’s Hospital of Anning City (Jinfang Branch), Anning, China
- 3Department of Medical Imaging, First People’s Hospital of Anning City (Jinfang Branch), Anning, China
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Breast cancer (BC) is the most common malignant tumor among women worldwide, posing a substantial threat to their health and overall quality of life. Consequently, for early-stage BC, timely screening, accurate diagnosis, and the development of personalized treatment strategies are crucial for enhancing patient survival rates. Automated Breast Ultrasound (ABUS) addresses the limitations of traditional handheld ultrasound (HHUS), such as operator dependency and inter-observer variability, by providing a more comprehensive and standardized approach to BC detection and diagnosis. Radiomics, an emerging field, focuses on extracting high-dimensional quantitative features from medical imaging data and utilizing them to construct predictive models for disease diagnosis, prognosis, and treatment evaluation. In recent years, the integration of artificial intelligence (AI) with radiomics has significantly enhanced the process of analyzing and extracting meaningful features from large and complex radiomic datasets through the application of machine learning (ML) and deep learning (DL) algorithms. Recently, AI-based ABUS radiomics has demonstrated significant potential in the diagnosis and therapeutic evaluation of BC. However, despite the notable performance and application potential of ML and DL models based on ABUS, the inherent variability in the analyzed data highlights the need for further evaluation of these models to ensure their reliability in clinical applications.
Keywords: Breast, Breast tumor, Automatic breast ultrasound, artificial intelligence, Radiomics, deep learning, machine learning
Received: 18 Feb 2025; Accepted: 14 Apr 2025.
Copyright: © 2025 Guo, Li, Song, Yang, Quan and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Hongjiang Zhang, First People’s Hospital of Anning City (Jinfang Branch), Anning, China
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