Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)

After publication of this article [1], the authors reported that Sira Sriswasdi should have been denoted as co-corresponding author. The original article [1] has been corrected.

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Bibliographic Details
Main Author:	Prayongrat A.
Other Authors:	Mahidol University
Format:	Erratum
Published:	2023
Subjects:	Medicine
Online Access:	https://repository.li.mahidol.ac.th/handle/123456789/82011
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Institution:	Mahidol University

id	th-mahidol.82011
record_format	dspace
spelling	th-mahidol.820112023-05-19T14:48:28Z Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8) Prayongrat A. Mahidol University Medicine After publication of this article [1], the authors reported that Sira Sriswasdi should have been denoted as co-corresponding author. The original article [1] has been corrected. 2023-05-19T07:48:28Z 2023-05-19T07:48:28Z 2023-12-01 Erratum Radiation Oncology Vol.18 No.1 (2023) 10.1186/s13014-023-02212-9 1748717X 36922889 2-s2.0-85150312095 https://repository.li.mahidol.ac.th/handle/123456789/82011 SCOPUS
institution	Mahidol University
building	Mahidol University Library
continent	Asia
country	Thailand Thailand
content_provider	Mahidol University Library
collection	Mahidol University Institutional Repository
topic	Medicine
spellingShingle	Medicine Prayongrat A. Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
description	After publication of this article [1], the authors reported that Sira Sriswasdi should have been denoted as co-corresponding author. The original article [1] has been corrected.
author2	Mahidol University
author_facet	Mahidol University Prayongrat A.
format	Erratum
author	Prayongrat A.
author_sort	Prayongrat A.
title	Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
title_short	Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
title_full	Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
title_fullStr	Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
title_full_unstemmed	Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
title_sort	correction: machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (albi) grade increase in hepatocellular carcinoma patients (radiation oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)
publishDate	2023
url	https://repository.li.mahidol.ac.th/handle/123456789/82011
_version_	1781414566738001920

Correction: Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients (Radiation Oncology, (2022), 17, 1, (202), 10.1186/s13014-022-02138-8)

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