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CORRECTION article

Front. Med., 22 November 2022
Sec. Translational Medicine
Volume 9 - 2022 | https://doi.org/10.3389/fmed.2022.1089705

Corrigendum: Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

This article is a correction to:

  1. Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

    1. Read original article
\nAnnarita FanizziAnnarita Fanizzi1Giovanni ScognamilloGiovanni Scognamillo1Alessandra NestolaAlessandra Nestola1Santa BambaceSanta Bambace2Samantha Bove Samantha Bove1*Maria Colomba Comes Maria Colomba Comes1*Cristian CristofaroCristian Cristofaro1Vittorio DidonnaVittorio Didonna1Alessia Di RitoAlessia Di Rito2Angelo ErricoAngelo Errico2Loredana PalermoLoredana Palermo1Pasquale TamborraPasquale Tamborra1Michele TroianoMichele Troiano3Salvatore ParisiSalvatore Parisi3Rossella VillaniRossella Villani1Alfredo ZitoAlfredo Zito1Marco LioceMarco Lioce1Raffaella MassafraRaffaella Massafra1
  • 1IRCCS Istituto Tumori “Giovanni Paolo II”, Bari, Italy
  • 2Ospedale Monsignor Raffaele Dimiccoli, Barletta, Italy
  • 3IRCCS Casa Sollievo della Sofferenza, Opera di San Pio da Pietrelcina Viale Cappuccini, Foggia, Italy

A corrigendum on
Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer

by Fanizzi, A., Scognamillo, G., Nestola, A., Bambace, S., Bove, S., Comes, M. C., Cristofaro, C., Didonna, V., Di Rito, A., Errico, A., Palermo, L., Tamborra, P., Troiano, M., Parisi, S., Villani, R., Zito, A., Lioce, M., and Massafra, R. (2022). Front. Med. 9:993395. doi: 10.3389/fmed.2022.993395

In the published article, there was an error in the Funding statement. The Funding statement for the Italian Ministry of Health was reported related to year “2002” as follows:

“This work was supported by funding from the Italian Ministry of Health, Ricerca Corrente 2002 Deliberation no. 219/2022, and Alleanza Contro il Cancro Association within the RADECISION project.”

The correct Funding statement appears below.

Funding

“This work was supported by funding from the Italian Ministry of Health, Ricerca Corrente 2022 Deliberation no. 219/2022, and Alleanza Contro il Cancro Association within the RADECISION project.”

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: deep learning, xerostomia, oropharyngeal cancer, CT images, CNN-convolutional neural network

Citation: Fanizzi A, Scognamillo G, Nestola A, Bambace S, Bove S, Comes MC, Cristofaro C, Didonna V, Di Rito A, Errico A, Palermo L, Tamborra P, Troiano M, Parisi S, Villani R, Zito A, Lioce M and Massafra R (2022) Corrigendum: Transfer learning approach based on computed tomography images for predicting late xerostomia after radiotherapy in patients with oropharyngeal cancer. Front. Med. 9:1089705. doi: 10.3389/fmed.2022.1089705

Received: 04 November 2022; Accepted: 07 November 2022;
Published: 22 November 2022.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2022 Fanizzi, Scognamillo, Nestola, Bambace, Bove, Comes, Cristofaro, Didonna, Di Rito, Errico, Palermo, Tamborra, Troiano, Parisi, Villani, Zito, Lioce and Massafra. 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: Samantha Bove, cy5ib3ZlJiN4MDAwNDA7b25jb2xvZ2ljby5iYXJpLml0; Maria Colomba Comes, bS5jLmNvbWVzJiN4MDAwNDA7b25jb2xvZ2ljby5iYXJpLml0

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.