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CORRECTION article

Front. Oncol., 07 September 2021
Sec. Thoracic Oncology
Volume 11 - 2021 | https://doi.org/10.3389/fonc.2021.747316

Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

This article is a correction to:

  1. Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

    1. Read original article
Guotao Yin&#x;Guotao Yin1†Ziyang Wang&#x;Ziyang Wang1†Yingchao Song&#x;Yingchao Song2†Xiaofeng LiXiaofeng Li1Yiwen ChenYiwen Chen1Lei ZhuLei Zhu1Qian SuQian Su1Dong Dai*Dong Dai1*Wengui Xu*Wengui Xu1*
  • 1Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin, China
  • 2School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China

A Corrigendum on
Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

By Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, Su Q, Dai D and Xu W (2021). Front. Oncol. 11:709137. doi: 10.3389/fonc.2021.709137

In the original article, there was a mistake in Table 2 as published. The clinical model was changed in the process of revising the manuscript. Due to our negligence, the AUC, sensitivity, and specificity of the clinical model for the training dataset were not correctly revised. The corrected Table 2 appears below.

TABLE 2
www.frontiersin.org

Table 2 Predictive performance of different models in the training dataset.

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: adenocarcinoma of lung, fluorodeoxyglucose F18, positron emission tomography computed tomography, deep learning, epidermal growth factor receptor

Citation: Yin G, Wang Z, Song Y, Li X, Chen Y, Zhu L, Su Q, Dai D and Xu W (2021) Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma. Front. Oncol. 11:747316. doi: 10.3389/fonc.2021.747316

Received: 26 July 2021; Accepted: 27 July 2021;
Published: 07 September 2021.

Edited and reviewed by:

Pasquale Pisapia, University of Naples Federico II, Italy

Copyright © 2021 Yin, Wang, Song, Li, Chen, Zhu, Su, Dai and Xu. 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: Dong Dai, tjdaidong@163.com; Wengui Xu, wenguixy@yeah.net

†These authors have contributed equally to this work and share first authorship

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.