Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning

Author:

Choi Seo Hee,Kim Euidam,Heo Seok-Jae,Seol Mi Youn,Chung Yoonsun,Yoon Hong In

Funder

Ministry of Science and Technology

Yonsei University College of Medicine

Publisher

Elsevier BV

Reference53 articles.

1. Machine learning for clinical outcome prediction;Shamout;IEEE Rev Biomed Eng,2021

2. Potential determinants for radiation-induced lymphopenia in patients with breast cancer using interpretable machine learning approach;Yu;Front Immunol,2022

3. Prediction of radiation-induced mucositis of H&N cancer patients based on a large patient cohort;Hansen;Radiother Oncol,2020

4. Biological dosiomic features for the prediction of radiation pneumonitis in esophageal cancer patients;Puttanawarut;Radiat Oncol,2021

5. Determining risk and predictors of head and neck cancer treatment-related lymphedema: a clinicopathologic and dosimetric data mining approach using interpretable machine learning and ensemble feature selection;Teo;Clin Transl Radiat Oncol,2024

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