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Corrigendum: Development of a Machine Learning Model for Optimal Applicator Selection in High-Dose-Rate Cervical Brachytherapy (Front. Oncol, (2021), 11, (611437), 10.3389/fonc.2021.611437)

  • Kailyn Stenhouse
  • , Michael Roumeliotis
  • , Philip Ciunkiewicz
  • , Robyn Banerjee
  • , Svetlana Yanushkevich
  • , Philip McGeachy

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Philip Ciunkiewicz was not included as an author in the published article. The corrected Author Contributions Statement appears below. KS and PC collected data, extracted features, deployed and evaluated machine learning algorithms, interpreted and visualized results, and drafted the manuscript. MR and PM conceptualized the project and participated in the study design, analysis and interpretation of results, and drafting of the manuscript. RB and SY provided expert guidance throughout the study and interpretation of results. All authors contributed to the article and approved the submitted version In the published article, there was an error regarding the affiliation for Philip Ciunkiewicz. They should have 4Department of Biomedical Engineering, University of Calgary, Calgary, AB, Canada. The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Original languageEnglish (US)
Article number730375
JournalFrontiers in Oncology
Volume11
DOIs
StatePublished - Jul 6 2021
Externally publishedYes

Keywords

  • decision-support tools
  • gynecologic brachytherapy
  • high-dose-rate brachytherapy
  • intracavitary brachytherapy
  • machine learning
  • radiation oncology

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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