Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

被引:0
作者
Gong, Xuantong [1 ]
Liu, Xuefeng [2 ]
Xie, Xiaozheng [3 ]
Wang, Yong [1 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Dept Ultrasound, Natl Canc Ctr, Natl Clin Res Ctr Canc,Canc Hosp, Beijing 100021, Peoples R China
[2] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Comp BDBC, Sch Comp Sci & Engn, State Key Lab Virtual Real Technol & Syst, Beijing, Peoples R China
[3] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing, Peoples R China
来源
CANCER INNOVATION | 2023年 / 2卷 / 04期
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
breast cancer; deep learning; prognosis prediction; radiomics; ultrasound; NEOADJUVANT CHEMOTHERAPY; IMAGING RADIOMICS;
D O I
10.1002/cai2.85
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women worldwide. Effective means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients' survival. Features extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's prognosis. Thus, radiomics provides a new approach to noninvasive assessment of breast cancer prognosis. Ultrasound is one of the commonest clinical means of examining breast cancer. In recent years, some results of research into ultrasound radiomics for diagnosing breast cancer, predicting lymph node status, treatment response, recurrence and survival times, and other aspects, have been published. In this article, we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer prognosis. We aim to provide a reference for radiomics researchers, promote the development of ultrasound radiomics, and advance its clinical application. Ultrasound radiomics is in the preliminary research stage to predict the prognosis of breast cancer. Multimodal prediction models that combine data from patients' clinical data, pathological results, gene expression, multiple images, and other data are the future research directions. High-quality studies can advance the clinical application of radiomics in the accurate diagnosis and treatment of breast cancer.1.2.3. image
引用
收藏
页码:283 / 289
页数:7
相关论文
共 50 条
[41]   A radiomics model based on transrectal ultrasound for predicting prostate cancer [J].
Huang, Yanhua ;
Qian, Hongwei ;
Zheng, Yuanyuan ;
Song, Huiming ;
Liu, Xiatian .
MEDICAL ULTRASONOGRAPHY, 2024, 26 (02) :138-146
[42]   Machine Learning Model for Predicting Axillary Lymph Node Metastasis in Clinically Node Positive Breast Cancer Based on Peritumoral Ultrasound Radiomics and SHAP Feature Analysis [J].
Wang, Si-Rui ;
Cao, Chun-Li ;
Du, Ting-Ting ;
Wang, Jin-Li ;
Li, Jun ;
Li, Wen-Xiao ;
Chen, Ming .
JOURNAL OF ULTRASOUND IN MEDICINE, 2024, 43 (09) :1611-1625
[43]   Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region [J].
Sun, Qiuchang ;
Lin, Xiaona ;
Zhao, Yuanshen ;
Li, Ling ;
Yan, Kai ;
Liang, Dong ;
Sun, Desheng ;
Li, Zhi-Cheng .
FRONTIERS IN ONCOLOGY, 2020, 10
[44]   Automated Breast Ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer [J].
Chen, Yu ;
Xie, Yongwei ;
Li, Bo ;
Shao, Hua ;
Na, Ziyue ;
Wang, Qiucheng ;
Jing, Hui .
BMC CANCER, 2023, 23 (01)
[45]   Automated Breast Ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer [J].
Yu Chen ;
Yongwei Xie ;
Bo Li ;
Hua Shao ;
Ziyue Na ;
Qiucheng Wang ;
Hui Jing .
BMC Cancer, 23
[46]   Research progress of radiomics and artificial intelligence in lung cancer [J].
Xiang Wang ;
Wenjun Huang ;
Jingyi Zhao ;
Shaochun Xu ;
Song Chen ;
Man Gao ;
Li Fan .
Chinese Journal of Academic Radiology, 2023, 6 (3) :91-99
[47]   Research progress of radiomics and artificial intelligence in lung cancer [J].
Wang, Xiang ;
Huang, Wenjun ;
Zhao, Jingyi ;
Xu, Shaochun ;
Chen, Song ;
Gao, Man ;
Fan, Li .
CHINESE JOURNAL OF ACADEMIC RADIOLOGY, 2023, 6 (03) :91-99
[48]   Delta Radiomics Based on MRI for Predicting Ancillary Lymph Node Pathologic Complete Response After Neoadjuvant Chemotherapy in Breast Cancer Patients [J].
Mao, Ning ;
Bao, Yuhan ;
Dong, Chuntong ;
Zhou, Heng ;
Zhang, Haicheng ;
Ma, Heng ;
Wang, Qi ;
Xie, Haizhu ;
Qu, Nina ;
Wang, Peiyuan ;
Lin, Fan ;
Lu, Jie .
ACADEMIC RADIOLOGY, 2025, 32 (01) :37-49
[49]   Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model [J].
Guo, Fuyu ;
Sun, Shiwei ;
Deng, Xiaoqian ;
Wang, Yue ;
Yao, Wei ;
Yue, Peng ;
Wu, Shaoduo ;
Yan, Junrong ;
Zhang, Xiaojun ;
Zhang, Yangang .
FRONTIERS IN IMMUNOLOGY, 2024, 15
[50]   Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer [J].
Xu, Maolin ;
Zeng, Shue ;
Li, Fang ;
Liu, Guifeng .
RADIOLOGIA MEDICA, 2024, 129 (01) :29-37