Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients

Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients

Abstract Purpose: The aim of this study was to develop a normal tissue complication probability model using a machine learning approach (ML-based NTCP) to predict the risk of radiation-induced liver disease in hepatocellular carcinoma (HCC) patients. Materials and methods: The study population inclu...

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Bibliographic Details
Main Authors: Anussara Prayongrat, Natchalee Srimaneekarn, Kanokporn Thonglert, Chonlakiet Khorprasert, Napapat Amornwichet, Petch Alisanant, Hiroki Shirato, Keiji Kobashi, Sira Sriswasdi
Format: Article
Language:English
Published: BMC 2022-12-01
Series:Radiation Oncology
Subjects:
Normal tissue complication probability model
Machine learning
Radiation-induced liver toxicity
Albumin-bilirubin grade
Hepatocellular carcinoma
Online Access:https://doi.org/10.1186/s13014-022-02138-8

Internet

https://doi.org/10.1186/s13014-022-02138-8

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