Guo Yinglin, Li Ning, Song Chonghui, Yang Juan, Quan Yinglan, Zhang Hongjiang
Faculty of Life Science and Technology & The Affiliated Anning First People's Hospital, Kunming University of Science and Technology, Kunming, China.
Department of Radiology, Faculty of Life Science and Technology & The Affiliated Anning First People's Hospital, Kunming University of Science and Technology, Kunming, China.
Front Oncol. 2025 May 8;15:1578991. doi: 10.3389/fonc.2025.1578991. eCollection 2025.
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.
乳腺癌（BC）是全球女性中最常见的恶性肿瘤，对她们的健康和整体生活质量构成了重大威胁。因此，对于早期乳腺癌，及时筛查、准确诊断以及制定个性化治疗策略对于提高患者生存率至关重要。自动乳腺超声（ABUS）通过提供一种更全面、标准化的乳腺癌检测和诊断方法，解决了传统手持超声（HHUS）的局限性，如对操作者的依赖和观察者间的差异。放射组学是一个新兴领域，专注于从医学影像数据中提取高维定量特征，并利用这些特征构建疾病诊断、预后和治疗评估的预测模型。近年来，人工智能（AI）与放射组学的结合通过应用机器学习（ML）和深度学习（DL）算法，显著增强了从大型复杂放射组学数据集中分析和提取有意义特征的过程。最近，基于AI的ABUS放射组学在乳腺癌的诊断和治疗评估中显示出巨大潜力。然而，尽管基于ABUS的ML和DL模型表现显著且具有应用潜力，但分析数据中固有的变异性凸显了对这些模型进行进一步评估以确保其在临床应用中的可靠性的必要性。
