Front. Med. Frontiers in Medicine Front. Med. 2296-858X Frontiers Media S.A. 10.3389/fmed.2025.1635819 Medicine Correction Correction: Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours Lu Guoxiu 1 2 Tian Ronghui 1 Yang Wei 3 Liu Ruibo 1 Liu Dongmei 4 Xiang Zijie 5 Zhang Guoxu 2 * 1College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, China 2Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, China 3Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China 4Department of Ultrasound, Beijing Shijitan Hospital, Capital Medical University, Beijing, China 5Biomedical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China

Edited and reviewed by: Minjeong Kim, University of North Carolina at Greensboro, United States

 *Correspondence: Guoxu Zhang zhangguoxu_502@163.com 23 06 2025 2025 12 1635819 27 05 2025 02 06 2025 Copyright © 2025 Lu, Tian, Yang, Liu, Liu, Xiang and Zhang. 2025 Lu, Tian, Yang, Liu, Liu, Xiang and Zhang

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 A Corrigendum on Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours by Lu, G., Tian, R., Yang, W., Liu, R., Liu, D., Xiang, Z., and Zhang, G. (2024). Front. Med. 11:1402967. doi: 10.3389/fmed.2024.1402967 deep learning radiomics multimodality imaging breast tumours deep learning radiomics MRI Mammography Ultrosonography section-at-acceptance Nuclear Medicine

In the published article, there was an error in [Figure 1. Flowchart of patient recruitment] as published. [A total of 322 patients enrolled in the study(including 122 benign tomors and 210 malignany tumors), Train set (n = 257) Benign = 96, malignant = 161].

The corrected [Figure 1. Flowchart of patient recruitment] and its caption [Figure 1. Flowchart of patient recruitment. A total of 322 patients enrolled in the study (including 112 benign tumors and 210 malignancy tumors), with the training set (n = 257) consisting of 89 benign tumors and 168 malignant tumors.]

Flowchart of patient recruitment. A total of 322 patients enrolled in the study (including 112 benign tumors and 210 malignancy tumors), with the training set (n = 257) consisting of 89 benign tumors and 168 malignant tumors.

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] appears below.

Characteristics of breast tumours in this study.

Characteristics Training (n = 257) Testing (n = 65) Values P
Menstrual status 89 (34.6%) 23 (35.4%) χ2 = 3.078 0.079
Age (years) 50.31 ± 11.57 51.08 ± 10.72 t = 0.486 0.627
Diameter (mm) 19.94 ± 11.27 22.78 ± 10.01 t = 1.476 0.141
CA-153 19.76 ± 8.97 20.52 ± 10.27 t = 0.593 0.554
BI-RADS category χ2 = 6.080 0.108
1–3 57 (22.2%) 24 (36.9%) - -
4 (4a,4b,4c) 138 (53.7%) 28 (43.1%) - -
5 44 (17.1%) 8 (12.3%) - -
6 18 (7.0%) 5 (7.7%)
Pathology χ2 = 0.087 0.768
Benign 89 (34.6%) 23 (35.4%) - -
Malignant 168 (65.4%) 42 (64.6%) - -

In the published article, there was an error. In Section 2.1 Patient population was published with 257 patients (96 with benign breast tumours and 161 with malignant breast tumours) enrolled in the training cohort. The corrected sentence appears below: [The training cohort included 257 patients (89 with benign and 168 with malignant breast tumors)]”.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

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