Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes

Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes

Abstract Background Medical imaging data frequently encounter image-generation heterogeneity and class imbalance properties, challenging strong generalized predictive performances with data-driven machine-learning methods. The purpose of this study was to investigate the impact of harmonization and...

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Bibliographic Details
Main Authors: Dongyang Du, Isaac Shiri, Fereshteh Yousefirizi, Mohammad R. Salmanpour, Jieqin Lv, Huiqin Wu, Wentao Zhu, Habib Zaidi, Lijun Lu, Arman Rahmim
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:EJNMMI Physics
Subjects:
PET radiomics
Harmonization
Oversampling
Multi-center imbalanced datasets
NSCLC
Online Access:https://doi.org/10.1186/s40658-025-00750-7
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https://doi.org/10.1186/s40658-025-00750-7

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