Front. Oncol. Frontiers in Oncology Front. Oncol. 2234-943X Frontiers Media S.A. 10.3389/fonc.2021.747316 Oncology Correction Corrigendum: Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma Yin Guotao 1 † Wang Ziyang 1 † Song Yingchao 2 † Li Xiaofeng 1 Chen Yiwen 1 Zhu Lei 1 Su Qian 1 Dai Dong 1 * Xu Wengui 1 * 1 Department 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 2 School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China

Edited and reviewed by: Pasquale Pisapia, University of Naples Federico II, Italy

*Correspondence: Dong Dai, tjdaidong@163.com; Wengui Xu, wenguixy@yeah.net

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

This article was submitted to Thoracic Oncology, a section of the journal Frontiers in Oncology

 07 09 2021 2021 11 747316 26 07 2021 27 07 2021 Copyright © 2021 Yin, Wang, Song, Li, Chen, Zhu, Su, Dai and Xu 2021 Yin, Wang, Song, Li, Chen, Zhu, Su, Dai and Xu

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

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.

Predictive performance of different models in the training dataset.

AUC (95% CI) Sensitivity (%) Specificity (%) Accuracy (%)
StackPET-CT 0.86 (0.80-0.91) 71.75 84.38 75.25
 SECT 0.74 (0.67-0.80) 82.35 53.12 67.17
 SEPET 0.75 (0.69-0.81) 86.25 56.25 72.22
Clinical model 0.63 (0.55-0.69) 50.98 71.88 60.10

The bold values represented the highest one of the evaluation indices.

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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