Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients

Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients

Background and purpose: Radiation-induced toxicities are common adverse events in lung cancer (LC) patients undergoing radiotherapy (RT). An accurate prediction of these adverse events might facilitate an informed and shared decision-making process between patient and radiation oncologist with a cle...

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Bibliographic Details
Main Authors: Francisco J. Núñez-Benjumea, Sara González-García, Alberto Moreno-Conde, José C. Riquelme-Santos, José L. López-Guerra
Format: Article
Language:English
Published: Elsevier 2023-07-01
Series:Clinical and Translational Radiation Oncology
Subjects:
Lung Neoplasms
Radiation-induced toxicity
Machine Learning
Learning Health System
Predictive Models
Online Access:http://www.sciencedirect.com/science/article/pii/S2405630823000654

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http://www.sciencedirect.com/science/article/pii/S2405630823000654

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