Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

Purpose: To establish the early prediction models of radiation-induced oral mucositis (RIOM) based on baseline CT-based radiomic features (RFs), dosimetric data, and clinical features by machine learning models for head and neck cancer (HNC) patients. Methods: In this single-center prospective study...

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Bibliographic Details
Main Authors: Razieh Agheli, Zahra Siavashpour, Reza Reiazi, Samira Azghandi, Susan Cheraghi, Reza Paydar
Format: Article
Language:English
Published: Elsevier 2024-02-01
Series:Heliyon
Subjects:
Radiomics
Machine learning
Toxicity prediction
Oral mucositis
Head and neck cancer
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844024008971

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http://www.sciencedirect.com/science/article/pii/S2405844024008971

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