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CORRECTION article

Front. Neurol., 10 May 2023
Sec. Neuro-Oncology and Neurosurgical Oncology
Volume 14 - 2023 | https://doi.org/10.3389/fneur.2023.1209701

Corrigendum: Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

This article is a correction to:

  1. Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

    1. Read original article
\r\nChenhua Luo,Chenhua Luo1,2Jiyan YangJiyan Yang2Zhengzheng LiuZhengzheng Liu1Di Jing Di Jing1*
  • 1Department of Oncology, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
  • 2Xiangya School of Medicine, Central South University, Changsha, China

A corrigendum on
Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

by Luo C., Yang J., Liu Z., and Jing D. (2023). Front. Neurol. 31:14:1100933. doi: 10.3389/fneur.2023.1100933

In the published article, there was an error in affiliations for Jiyan Yang, Zhengzheng Liu and Di Jing. Instead of “Chenhua Luo1, 2, Jiyan Yang1, Zhengzheng Liu2 and Di Jing2*”, it should be “Chenhua Luo1, 2, Jiyan Yang2, Zhengzheng Liu1 and Di Jing1*”.

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.

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: glioma, pathomics, deep learning, recurrence, overall survival

Citation: Luo C, Yang J, Liu Z and Jing D (2023) Corrigendum: Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning. Front. Neurol. 14:1209701. doi: 10.3389/fneur.2023.1209701

Received: 21 April 2023; Accepted: 24 April 2023;
Published: 10 May 2023.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2023 Luo, Yang, Liu and Jing. 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: Di Jing, amluZ2RpJiN4MDAwNDA7eGlhbmd5YS5jb20uY24=

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.