MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

Background: The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemora...

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Bibliographic Details
Main Authors: Bao, H. (Author), Chen, X. (Author), Huang, X. (Author), Jin, G. (Author), Liang, X. (Author), Liao, H. (Author), Pei, W. (Author), Su, D. (Author), Wang, C. (Author), Wei, Y. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
Machine learning
Magnetic resonance imaging
Nasopharyngeal carcinoma
Radiomics
Radom survival forest
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