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Cancer Innovation 2023, 2(4): 283-289 https://doi.org/10.1002/cai2.85
Review | Open Access | Issue | Published: 11 July 2023

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

Show Author's Information Xuantong Gong1,  Xuefeng Liu2,  Xiaozheng Xie3,  Yong Wang1( )
Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
Keywords:
deep learning, breast cancer, ultrasound, radiomics, prognosis prediction
Cite this article:
Gong X, Liu X, Xie X, et al. Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer. Cancer Innovation, 2023, 2(4): 283-289. https://doi.org/10.1002/cai2.85
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Abstract Full text About this article

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.


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About this article

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

Show Author's information Xuantong Gong1, Xuefeng Liu2, Xiaozheng Xie3, Yong Wang1( )
Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China

Abstract

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.

Keywords: deep learning, breast cancer, ultrasound, radiomics, prognosis prediction

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Received: 26 February 2023
Accepted: 09 June 2023
Published: 11 July 2023
Issue date: August 2023

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© 2023 The Authors. Tsinghua University Press.

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