Ultrasound radiomics in personalized breast management: Current status and future prospects

被引:23
作者
Gu, Jionghui [1 ,2 ,3 ]
Jiang, Tian'an [1 ,2 ,3 ]
机构
[1] Zhejiang Univ, Affiliated Hosp 1, Coll Med, Dept Ultrasound, Hangzhou, Peoples R China
[2] Key Lab Pulsed Power Translat Med Zhejiang Prov, Hangzhou, Peoples R China
[3] Zhejiang Univ, Canc Ctr, Hangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
ultrasound; radiomics; breast; personalized medicine; artificial intelligence; PATHOLOGICAL COMPLETE RESPONSE; DEEP-LEARNING-METHOD; NEOADJUVANT CHEMOTHERAPY; CANCER; PREDICTION; MRI; METAANALYSIS; SURGERY; MODEL;
D O I
10.3389/fonc.2022.963612
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Breast cancer is the most common cancer in women worldwide. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies are essential steps in achieving precision medicine before, during and after treatment. Radiomics provides image information that cannot be recognized by the naked eye through deep mining of medical images. Several studies have shown that radiomics, as a second reader of medical images, can assist physicians not only in the detection and diagnosis of breast lesions but also in the assessment of risk stratification and prediction of treatment response. Recently, more and more studies have focused on the application of ultrasound radiomics in breast management. We summarized recent research advances in ultrasound radiomics for the diagnosis of benign and malignant breast lesions, prediction of molecular subtype, assessment of lymph node status, prediction of neoadjuvant chemotherapy response, and prediction of survival. In addition, we discuss the current challenges and future prospects of ultrasound radiomics.
引用
收藏
页数:10
相关论文
共 74 条
[1]   Breast Imaging Reporting and Data System Lexicon for US: Interobserver Agreement for Assessment of Breast Masses [J].
Abdullah, Nouf ;
Mesurolle, Benoit ;
El-Khoury, Mona ;
Kao, Ellen .
RADIOLOGY, 2009, 252 (03) :665-672
[2]   Artificial intelligence as the next step towards precision pathology [J].
Acs, B. ;
Rantalainen, M. ;
Hartman, J. .
JOURNAL OF INTERNAL MEDICINE, 2020, 288 (01) :62-81
[3]   Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach [J].
Aerts, Hugo J. W. L. ;
Velazquez, Emmanuel Rios ;
Leijenaar, Ralph T. H. ;
Parmar, Chintan ;
Grossmann, Patrick ;
Cavalho, Sara ;
Bussink, Johan ;
Monshouwer, Rene ;
Haibe-Kains, Benjamin ;
Rietveld, Derek ;
Hoebers, Frank ;
Rietbergen, Michelle M. ;
Leemans, C. Rene ;
Dekker, Andre ;
Quackenbush, John ;
Gillies, Robert J. ;
Lambin, Philippe .
NATURE COMMUNICATIONS, 2014, 5
[4]   Cost Modeling of Preoperative Axillary Ultrasound and Fine-Needle Aspiration to Guide Surgery for Invasive Breast Cancer [J].
Boughey, Judy C. ;
Moriarty, James P. ;
Degnim, Amy C. ;
Gregg, Melissa S. ;
Egginton, Jason S. ;
Long, Kirsten Hall .
ANNALS OF SURGICAL ONCOLOGY, 2010, 17 (04) :953-958
[5]   Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks [J].
Byra, Michal ;
Dobruch-Sobczak, Katarzyna ;
Klimonda, Ziemowit ;
Piotrzkowska-Wroblewska, Hanna ;
Litniewski, Jerzy .
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2021, 25 (03) :797-805
[6]   Deep Learning and Its Applications in Biomedicine [J].
Cao, Chensi ;
Liu, Feng ;
Tan, Hai ;
Song, Deshou ;
Shu, Wenjie ;
Li, Weizhong ;
Zhou, Yiming ;
Bo, Xiaochen ;
Xie, Zhi .
GENOMICS PROTEOMICS & BIOINFORMATICS, 2018, 16 (01) :17-32
[7]   AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics [J].
Castiglioni, Isabella ;
Gallivanone, Francesca ;
Soda, Paolo ;
Avanzo, Michele ;
Stancanello, Joseph ;
Aiello, Marco ;
Interlenghi, Matteo ;
Salvatore, Marco .
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2019, 46 (13) :2673-2699
[8]   Impact of radiomics on the breast ultrasound radiologist's clinical practice: From lumpologist to data wrangler [J].
Castro Fleury, Eduardo de Faria ;
Marcomini, Karen .
EUROPEAN JOURNAL OF RADIOLOGY, 2020, 131
[9]   Domain Knowledge Powered Deep Learning for Breast Cancer Diagnosis Based on Contrast-Enhanced Ultrasound Videos [J].
Chen, Chen ;
Wang, Yong ;
Niu, Jianwei ;
Liu, Xuefeng ;
Li, Qingfeng ;
Gong, Xuantong .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2021, 40 (09) :2439-2451
[10]   Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning [J].
Chen, Yiwen ;
Wang, Ziyang ;
Yin, Guotao ;
Sui, Chunxiao ;
Liu, Zifan ;
Li, Xiaofeng ;
Chen, Wei .
ANNALS OF NUCLEAR MEDICINE, 2022, 36 (02) :172-182