Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
Department of Radiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia.
Acad Radiol. 2022 Jan;29 Suppl 1:S89-S106. doi: 10.1016/j.acra.2021.07.017. Epub 2021 Sep 2.
Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.
We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.
All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.
The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard. Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.
磁共振成像（MRI）是检测乳腺癌最敏感的成像方式。本系统评价的目的是研究人类提取的 MRI 表型在分类乳腺癌分子亚型中的作用。
我们对 2000 年 1 月 1 日至 2021 年 3 月 31 日在 Medline Complete、Pubmed 和 Google Scholar 上发表的关于放射科医生解读 MRI 表型特征在浸润性乳腺癌分子亚型分类中的应用的文献进行了检索。在确定的 1453 篇文献中，有 42 篇符合纳入标准。
所有研究均为病例对照、回顾性研究和基于研究的研究。大多数研究使用美国放射学院-乳腺成像报告和数据系统（ACR-BIRADS）分类评估 MRI 特征，并使用动态对比增强（DCE）动力学特征、表观扩散系数（ADC）值和 T2 序列。大多数研究将浸润性乳腺癌分为 4 种主要亚型：luminal A、luminal B、HER2 和三阴性（TN）癌症，并使用 2 名读者。我们总结了放射科医生提取的乳腺 MRI 表型特征及其与乳腺癌亚型分类的相关性。特征性的特征是形态、增强动力学和 T2 信号强度。我们发现，与其他亚型相比，TN 亚型具有最独特的 MRI 特征，而 luminal A 和 B 具有许多相似的特征。
预测每种亚型的 MRI 特征是形态、内部增强特征和 T2 信号强度，主要是在 TN 与其他亚型之间。放射科医生对某些 MRI 特征的视觉解读可能有助于了解相应的浸润性乳腺癌分子亚型。然而，目前的证据仍然仅限于“提示性”特征，而不是诊断标准。建议进一步研究以探索这种潜在的应用，例如通过人工智能增强放射科医生的视觉解读。
