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CORRECTION article

Front. Med.

Sec. Nuclear Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1635819

Correction: Deep learning radiomics based on multimodal imaging for distinguishing benign

Provisionally accepted
Guoxiu LuGuoxiu Lu1*Ronghui TianRonghui Tian1Wei YangWei Yang2Ruibo LiuRuibo Liu1Dongmei LiuDongmei Liu3,4Zijie XiangZijie Xiang5Zhang GuoxuZhang Guoxu6
  • 1Department of Nuclear Medicine, General Hospital of Northern Theater Command, School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China
  • 2Liaoning Cancer Hospital and Institute, Shenyang, China
  • 3Beijing Shijitan Hospital Capital Medical University, Beijing, China
  • 4Department of Ultrasound, Beijing Shijitan Hospital, Capital Medical University, Beijing, China, Beijing, China
  • 5Biomedical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China, Shenyang, China
  • 6General Hospital of Eastern Theatre Command, Nanjing, China

This article is a correction to:

  1. Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours

    1. Read original article

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In our published article:"Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumour".There were some mistakes in the Figure 1,Table1 and Section 2.1-Patient population.In the figure 1 In the published article, there was an error in [Table 1 Characteristics of breast tumors in this study:in the training columns,Benign:96(37.4%),Malignant:161(62. 6%)] as published.The corrected [Table 1 Characteristics of breast tumors in this study.Benign:89(34.6%),Malignant:168(65.4 %)] and its caption [in the training columns, ] appear below.

Keywords: deep learning, Radiomics, multimodality imaging, Breast tumours, Deep learning radiomics, MRI, Mammography, Ultrosonography

Received: 27 May 2025; Accepted: 02 Jun 2025.

Copyright: © 2025 Lu, Tian, Yang, Liu, Liu, Xiang and Guoxu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Guoxiu Lu, Department of Nuclear Medicine, General Hospital of Northern Theater Command, School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material