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CORRECTION article

Front. Oncol., 03 April 2024
Sec. Genitourinary Oncology
Volume 14 - 2024 | https://doi.org/10.3389/fonc.2024.1401121

Corrigendum: Radiomic machine learning and external validation based on 3.0T mpMRI for prediction of intraductal carcinoma of prostate with different proportion

This article is a correction to:

  1. Radiomic Machine Learning and External Validation Based on 3.0 T mpMRI for Prediction of Intraductal Carcinoma of Prostate With Different Proportion

    1. Read original article
Ling Yang&#x;Ling Yang1†Zhengyan Li&#x;Zhengyan Li1†Xu LiangXu Liang2Jingxu XuJingxu Xu3Yusen CaiYusen Cai3Chencui HuangChencui Huang3Mengni ZhangMengni Zhang4Jin Yao*&#x;Jin Yao1*‡Bin Song*&#x;Bin Song1*‡
  • 1Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
  • 2Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
  • 3Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China
  • 4Department of Pathology, West China Hospital of Sichuan University, Chengdu, China

A Corrigendum on
Radiomic machine learning and external validation based on 3.0 T mpMRI for prediction of intraductal carcinoma of prostate with different proportion

By Yang L, Li Z, Liang X, Xu J, Cai Y, Huang C, Zhang M, Yao J and Song B (2022) Front. Oncol. 12:934291. doi: 10.3389/fonc.2022.934291

In the published article, there was an error in the Funding statement for Science and Technology Support Program of Sichuan Province (No. 22NSFSC117). The correct Funding statement appears below.

FUNDING

This work was supported by the 1*3*5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (No. ZY2017304), Science and Technology Support Program of Sichuan Province (No. 2022NSFSC0840) and the sky imaging research foundation (Z-2014-07-1912).

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: radiomics, machine learning, prostate cancer, intraductal carcinoma, multiparametric MRI

Citation: Yang L, Li Z, Liang X, Xu J, Cai Y, Huang C, Zhang M, Yao J and Song B (2024) Corrigendum: Radiomic machine learning and external validation based on 3.0T mpMRI for prediction of intraductal carcinoma of prostate with different proportion. Front. Oncol. 14:1401121. doi: 10.3389/fonc.2024.1401121

Received: 14 March 2024; Accepted: 18 March 2024;
Published: 03 April 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2024 Yang, Li, Liang, Xu, Cai, Huang, Zhang, Yao and Song. 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: Jin Yao, c2hlbGxleXlhb0AxNjMuY29t; Bin Song, c29uZ2xhYl9yYWRpb2xvZ3lAMTYzLmNvbQ==

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

‡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.