In the published article “Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res.(2018)15;78(16):4786-89. doi: 10.1158/0008-5472.CAN-18-0125.” was not cited in the article. The citation has now been inserted in Section 2.6, 2.6 Data pre-processing and should read:
“In the context of image preprocessing for CT data, the original images are first adjusted to a window width and level setting of −500 and 1,500, respectively. Following this adjustment, the data undergo a 1 × 1 × 1 resampling process. Subsequently, the CT images are cropped on the basis of the maximal edge range of the 3D ROI. For each case, eight central images from the lesion area are retained as input data (the eight central images of the lesion refer to the eightlayer coronal images of the central slice of the 3D lesion). Similarly, in the case of PET images, after completing the SUV conversion using LIFEx software (version v6.20) (22), the images are cropped on the basis of the maximal edge of the 3D ROI. Again, eight central images from the lesion are preserved as input data for each case. If the number of lesion slices is less than eight, then all slices of data are kept as input. The images are then resized to 299 × 299 pixels to satisfy the input requirements of the CNN model. The data are randomly stratified into training and validation sets in a 7:3 ratio.”
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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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.
Summary
Keywords
18F-FDG PET/CT, lung adenocarcinoma, EGFR, mutation, deep learning
Citation
Huang L, Kong W, Luo Y, Xie H, Liu J, Zhang X and Zhang G (2025) Corrigendum: Predicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning. Front. Oncol. 15:1564325. doi: 10.3389/fonc.2025.1564325
Received
21 January 2025
Accepted
30 January 2025
Published
12 February 2025
Approved by
Frontiers Editorial Office, Frontiers Media SA, Switzerland
Volume
15 - 2025
Updates
Copyright
© 2025 Huang, Kong, Luo, Xie, Liu, Zhang 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: Guojin Zhang, zhanggj15@lzu.edu.cn
†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.