文章：

机器学习评估乳腺癌背景实质强化及其临床应用：文献综述

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review

原文发布日期：31 October 2024

DOI: 10.3390/cancers16213681

类型: Article

开放获取: 是

英文摘要：

Background Parenchymal Enhancement (BPE) on breast MRI holds promise as an imaging biomarker for breast cancer risk and prognosis. The ability to identify those at greatest risk can inform clinical decisions, promoting early diagnosis and potentially guiding strategies for prevention such as risk-reduction interventions with the use of selective estrogen receptor modulators and aromatase inhibitors. Currently, the standard method of assessing BPE is based on the Breast Imaging-Reporting and Data System (BI-RADS), which involves a radiologist’s qualitative categorization of BPE as minimal, mild, moderate, or marked on contrast-enhanced MRI. This approach can be subjective and prone to inter/intra-observer variability, and compromises accuracy and reproducibility. In addition, this approach limits qualitative assessment to 4 categories. More recently developed methods using machine learning/artificial intelligence (ML/AI) techniques have the potential to quantify BPE more accurately and objectively. This paper will review the current machine learning/AI methods to determine BPE, and the clinical applications of BPE as an imaging biomarker for breast cancer risk prediction and prognosis.

摘要翻译： 

背景：乳腺磁共振成像中的背景实质强化（BPE）有望成为乳腺癌风险与预后的影像学生物标志物。准确识别高风险人群可为临床决策提供依据，促进早期诊断，并可能指导预防策略，例如使用选择性雌激素受体调节剂和芳香化酶抑制剂进行风险降低干预。目前，评估BPE的标准方法基于乳腺影像报告和数据系统（BI-RADS），由放射科医生在对比增强磁共振成像中将BPE定性分为轻微、轻度、中度或显著四级。这种方法具有主观性，易受观察者间及观察者内变异影响，从而影响其准确性和可重复性。此外，该方法将定性评估局限于四个类别。近年来发展的机器学习/人工智能（ML/AI）技术有望更准确、客观地量化BPE。本文将综述当前用于测定BPE的机器学习/AI方法，以及BPE作为影像学生物标志物在乳腺癌风险预测和预后评估中的临床应用。

原文链接：

Machine Learning Assessment of Background Parenchymal Enhancement in Breast Cancer and Clinical Applications: A Literature Review