Assessment of breast composition in MRI using artificial intelligence – A systematic review

IF 2.5 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-02-20 DOI:10.1016/j.radi.2025.102900

P.C. Murphy , M. McEntee , M. Maher , M.F. Ryan , C. Harman , A. England , N. Moore

{"title":"Assessment of breast composition in MRI using artificial intelligence – A systematic review","authors":"P.C. Murphy , M. McEntee , M. Maher , M.F. Ryan , C. Harman , A. England , N. Moore","doi":"10.1016/j.radi.2025.102900","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation of artificial intelligence (AI). AI assessment of breast composition factors is less studied than those of lesion detection and classification. These factors are breast density, background parenchymal enhancement (BPE) and fibroglandular tissue (FGT), which are recognized breast cancer phenotypes.</div></div><div><h3>Methods</h3><div>Following PRISMA guidelines, the PROSPERO registered review examined the role of AI in assessing breast composition in MRI. A search of articles from Pubmed, Ovid, Embase, Web of Science, Cochrane, and Google scholar from 2010 to 2022 was conducted. Peer-reviewed, in-vivo studies were included based on defined search categories. Adapted QUADAS-2, CASP and Covidence tools were utilized for quality assessment.</div></div><div><h3>Results</h3><div>Seven studies were identified as being of sufficiently high quality. The studies showed that AI has the potential to provide a comparable level of accuracy against the relevant reference standard. There were limited performance results when delineating BPE and FGT BI-RADs categories. The review highlighted the variability in AI models while the range of statistical methods and small cohort sizes limited cross study compatibility.</div></div><div><h3>Conclusions</h3><div>AI has potential in assessing breast composition in MRI. However, variability in AI systems deployed and statistical measurements alongside limited validation across diverse populations remain an issue. AI systems may perform better with binary categorizations rather than the quaternary spectrum of BI-RADS.</div></div><div><h3>Implications for practice</h3><div>AI could assist in developing personalized breast composition assessments. Future developments could focus on better delineation of breast composition categories. AI models that have trained on more diverse and larger populations should result in more robust and effective clinical applications.</div></div>","PeriodicalId":47416,"journal":{"name":"Radiography","volume":"31 2","pages":"Article 102900"},"PeriodicalIF":2.5000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiography","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1078817425000410","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction

Magnetic Resonance Imaging (MRI) performs a critical role in breast cancer diagnosis, especially for high-risk populations e.g. family history. MRI could take advantage of the implementation of artificial intelligence (AI). AI assessment of breast composition factors is less studied than those of lesion detection and classification. These factors are breast density, background parenchymal enhancement (BPE) and fibroglandular tissue (FGT), which are recognized breast cancer phenotypes.

Methods

Following PRISMA guidelines, the PROSPERO registered review examined the role of AI in assessing breast composition in MRI. A search of articles from Pubmed, Ovid, Embase, Web of Science, Cochrane, and Google scholar from 2010 to 2022 was conducted. Peer-reviewed, in-vivo studies were included based on defined search categories. Adapted QUADAS-2, CASP and Covidence tools were utilized for quality assessment.

Results

Seven studies were identified as being of sufficiently high quality. The studies showed that AI has the potential to provide a comparable level of accuracy against the relevant reference standard. There were limited performance results when delineating BPE and FGT BI-RADs categories. The review highlighted the variability in AI models while the range of statistical methods and small cohort sizes limited cross study compatibility.

Conclusions

AI has potential in assessing breast composition in MRI. However, variability in AI systems deployed and statistical measurements alongside limited validation across diverse populations remain an issue. AI systems may perform better with binary categorizations rather than the quaternary spectrum of BI-RADS.

Implications for practice

AI could assist in developing personalized breast composition assessments. Future developments could focus on better delineation of breast composition categories. AI models that have trained on more diverse and larger populations should result in more robust and effective clinical applications.

Abstract Image

查看原文本刊更多论文

利用人工智能评估MRI中的乳腺成分-系统综述

磁共振成像（MRI）在乳腺癌诊断中起着至关重要的作用，特别是对高危人群，如家族史。MRI可以利用人工智能（AI）的实施。人工智能对乳腺组成因素的评价相对于病变的检测和分类研究较少。这些因素是乳腺密度，背景实质增强（BPE）和纤维腺组织（FGT），这是公认的乳腺癌表型。方法遵循PRISMA指南，PROSPERO注册综述研究了AI在MRI评估乳腺成分中的作用。检索Pubmed、Ovid、Embase、Web of Science、Cochrane和谷歌scholar 2010 - 2022年的文章。同行评审的体内研究被纳入定义的搜索类别。采用适应性QUADAS-2、CASP和covid工具进行质量评估。结果7项研究被确定为足够高质量。研究表明，人工智能有可能提供与相关参考标准相当的准确性。在划定BPE和FGT BI-RADs类别时，性能结果有限。该综述强调了人工智能模型的可变性，而统计方法的范围和小队列规模限制了交叉研究的兼容性。结论sai在MRI评估乳腺成分方面具有一定的应用价值。然而，人工智能系统部署的可变性和统计测量以及在不同人群中的有限验证仍然是一个问题。与BI-RADS的四元光谱相比，人工智能系统在二元分类方面可能表现更好。人工智能对实践的启示可以帮助开发个性化的乳房成分评估。未来的发展可以集中在更好地描绘乳房成分类别。在更多样化和更大的人群中训练的人工智能模型应该会产生更强大和有效的临床应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.