Skip to main content

CORRECTION article

Front. Med., 14 May 2024
Sec. Nuclear Medicine
Volume 11 - 2024 | https://doi.org/10.3389/fmed.2024.1421603

Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

This article is a correction to:

  1. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

    1. Read original article
\r\nBao Ngoc HuynhBao Ngoc Huynh1Aurora Rosvoll GroendahlAurora Rosvoll Groendahl1Oliver TomicOliver Tomic1Kristian Hovde LilandKristian Hovde Liland1Ingerid Skjei Knudtsen,Ingerid Skjei Knudtsen2,3Frank Hoebers,Frank Hoebers4,5Wouter van Elmpt,Wouter van Elmpt4,5Eirik Malinen,Eirik Malinen3,6Einar DaleEinar Dale7Cecilia Marie Futsaether Cecilia Marie Futsaether1*
  • 1Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
  • 2Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
  • 3Department of Medical Physics, Oslo University Hospital, Oslo, Norway
  • 4Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
  • 5GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
  • 6Department of Physics, University of Oslo, Oslo, Norway
  • 7Department of Oncology, Oslo University Hospital, Oslo, Norway

A corrigendum on
Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics

by Huynh, B. N., Groendahl, A. R., Tomic, O., Liland, K. H., Knudtsen, I. S., Hoebers, F., van Elmpt, W., Malinen, E., Dale, E., and Futsaether, C. M. (2023). Front. Med. 10:1217037. doi: 10.3389/fmed.2023.1217037

In the published article, there was an error in Supplementary Tables F1, F2. Incorrect numbers were inserted into the two specificity columns. All other columns are correct.

The corrected Supplementary Tables F1, F2 has been published in the original article.

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way and does not change the original article in any way. The original Supplementary material file 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: machine learning, deep learning, artificial intelligence, feature selection, radiomics, head and neck cancer, interpretability, outcome prediction

Citation: Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E and Futsaether CM (2024) Corrigendum: Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics. Front. Med. 11:1421603. doi: 10.3389/fmed.2024.1421603

Received: 22 April 2024; Accepted: 06 May 2024;
Published: 14 May 2024.

Edited and reviewed by: Giulia Besutti, IRCCS Local Health Authority of Reggio Emilia, Italy

Copyright © 2024 Huynh, Groendahl, Tomic, Liland, Knudtsen, Hoebers, van Elmpt, Malinen, Dale and Futsaether. 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: Cecilia Marie Futsaether, Y2VjaWxpYS5mdXRzYWV0aGVyJiN4MDAwNDA7bm1idS5ubw==

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.