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CORRECTION article

Front. Oncol., 26 August 2025

Sec. Cancer Imaging and Image-directed Interventions

Volume 15 - 2025 | https://doi.org/10.3389/fonc.2025.1674168

This article is part of the Research TopicAdvances in Intelligence or Nanomedicine-based Theranostics for CancersView all 4 articles

Correction: Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics

This article is a correction to:

  1. Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics

    1. Read original article
Xiaobo Wen,,&#x;Xiaobo Wen1,2,3†Yutao Zhao&#x;Yutao Zhao2†Wen Dong&#x;Wen Dong2†Congbo YangCongbo Yang2Jinzhi LiJinzhi Li2Li SunLi Sun3Yutao XiuYutao Xiu3Chang&#x;e Gao*Chang’e Gao4*Ming Zhang*Ming Zhang2*
  • 1School of Pharmacy, Qingdao University, Qingdao, China
  • 2Department of Radiotherapy, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
  • 3Cancer Institute of The Affiliated Hospital of Qingdao University and Qingdao Cancer Institute, Qingdao, China
  • 4Department of Medical Oncology, The First Affiliated Hospital of Kunming Medical University, Kunming, China

A Correction on
Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics

By Wen X, Zhao Y, Dong W, Yang C, Li J, Sun L, Xiu Y, Gao C and Zhang M (2025). Front. Oncol. 15:1609421. doi: 10.3389/fonc.2025.1609421

In the published article, there was an error in the legend for Figure 4 as published. The original legend did not match the actual content of the figure. The corrected legend appears below.

Figure 4
Plot showing Mean Squared Error (MSE) on the y-axis and Lambda on the x-axis, ranging from 10^-3 to 10^0. Red dots represent data points with blue vertical error bars. A vertical dashed line indicates the optimal Lambda value at 0.0391. MSE decreases initially, then stabilizes.

Figure 4. The MSE of LASSO regression.

“The MSE of LASSO regression.”

The original version of this article has been updated.

Publisher’s note

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.

Keywords: breast cancer, radiomics, radiation pneumonitis, machine learning, artificial intelligence

Citation: Wen X, Zhao Y, Dong W, Yang C, Li J, Sun L, Xiu Y, Gao C and Zhang M (2025) Correction: Personalized diagnosis of radiation pneumonitis in breast cancer patients based on radiomics. Front. Oncol. 15:1674168. doi: 10.3389/fonc.2025.1674168

Received: 29 July 2025; Accepted: 12 August 2025;
Published: 26 August 2025.

Edited and reviewed by:

Han Wang, Shanghai Jiao Tong University School Medicine, China

Copyright © 2025 Wen, Zhao, Dong, Yang, Li, Sun, Xiu, Gao and Zhang. 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) and the copyright owner(s) 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: Ming Zhang, emhhbmdtaW5nMUBrbW11LmVkdS5jbg==; Chang’e Gao, Z2FvY2hhbmdlQGttbXUuZWR1LmNu

†These authors have contributed equally to this work

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.