Deep learning, radiomics and radiogenomics applications in the digital breast tomosynthesis: a systematic review

BMC Bioinformatics

Research

Usman Naseem¹  Mansoor Ali²  Sadam Hussain²  Yareth Lafarga-Osuna²  Masroor Ahmed²  Jose Gerardo Tamez-Peña³ 

[1] College of Science and Engineering, James Cook University, Cairns, Australia;School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico;School of Medicine and Health Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico;

关键词: Deep learning;    Radiomics;    Radiogenomics;    Digital breast tomosynthesis;    Breast cancer;    Lesion detection;    Lesion classification;    Medical imaging;   

DOI  :  10.1186/s12859-023-05515-6

 received in 2023-04-07, accepted in 2023-10-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundRecent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images.Objective and methodsThis paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers.ResultsA total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images.ConclusionUsing this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 1

Fig. 6

Fig. 2

12951_2017_255_Article_IEq39.gif

Fig. 4

Fig. 3

12936_2017_2075_Article_IEq66.gif