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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SpringerOpen
2025-04-01
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| Series: | EJNMMI Physics |
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| Online Access: | https://doi.org/10.1186/s40658-025-00750-7 |
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| author | Dongyang Du Isaac Shiri Fereshteh Yousefirizi Mohammad R. Salmanpour Jieqin Lv Huiqin Wu Wentao Zhu Habib Zaidi Lijun Lu Arman Rahmim |
| author_facet | Dongyang Du Isaac Shiri Fereshteh Yousefirizi Mohammad R. Salmanpour Jieqin Lv Huiqin Wu Wentao Zhu Habib Zaidi Lijun Lu Arman Rahmim |
| author_sort | Dongyang Du |
| collection | DOAJ |
| description | 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 oversampling methods on multi-center imbalanced datasets, with specific application to PET-based radiomics modeling for histologic subtype prediction in non-small cell lung cancer (NSCLC). Methods The retrospective study included 245 patients with adenocarcinoma (ADC) and 78 patients with squamous cell carcinoma (SCC) from 4 centers. Utilizing 1502 radiomics features per patient, we trained, validated, and tested 4 machine-learning classifiers, to investigate the effect of no harmonization (NoH) or 4 feature harmonization methods, paired with no oversampling (NoO) or 5 oversampling methods on subtype prediction. Model performance was evaluated using the average area under the ROC curve (AUROC) and G-mean via 5 times 5-fold cross-validations. Statistical comparisons of the combined models against baseline (NoH + NoO) were performed for each fold of cross-validation using the DeLong test. Results The number of cross-combinations with both AUROC and G-mean outperforming baseline in validation and testing was 15, 4, 2, and 7 (out of 29) for random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM), respectively. ComBat harmonization combined with oversampling (SMOTE) via RF yielded better performance than baseline (AUROC and G-mean of validation: 0.725 vs. 0.608 and 0.625 vs. 0.398; testing: 0.637 vs. 0.567 and 0.506 vs. 0.287), though statistical significances were not observed. Conclusions Applying harmonization and oversampling methods in multi-center imbalanced datasets can improve NSCLC-subtype prediction, but the effect varies widely across classifiers. We have created open-source comparisons of harmonization and oversampling on different classifiers for comprehensive evaluations in different studies. |
| format | Article |
| id | doaj-art-ed62b6da2fa14ea6b14289164fa5bbfd |
| institution | DOAJ |
| issn | 2197-7364 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EJNMMI Physics |
| spelling | doaj-art-ed62b6da2fa14ea6b14289164fa5bbfd2025-08-20T03:06:52ZengSpringerOpenEJNMMI Physics2197-73642025-04-0112111610.1186/s40658-025-00750-7Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypesDongyang Du0Isaac Shiri1Fereshteh Yousefirizi2Mohammad R. Salmanpour3Jieqin Lv4Huiqin Wu5Wentao Zhu6Habib Zaidi7Lijun Lu8Arman Rahmim9College of Computer Science, Inner Mongolia UniversityDivision of Nuclear Medicine and Molecular Imaging, Geneva University HospitalDepartment of Integrative Oncology, BC Cancer Research InstituteDepartment of Integrative Oncology, BC Cancer Research InstituteSchool of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical UniversitySchool of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical UniversityResearch Center for Healthcare Data ScienceDivision of Nuclear Medicine and Molecular Imaging, Geneva University HospitalSchool of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical UniversityDepartment of Integrative Oncology, BC Cancer Research InstituteAbstract 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 oversampling methods on multi-center imbalanced datasets, with specific application to PET-based radiomics modeling for histologic subtype prediction in non-small cell lung cancer (NSCLC). Methods The retrospective study included 245 patients with adenocarcinoma (ADC) and 78 patients with squamous cell carcinoma (SCC) from 4 centers. Utilizing 1502 radiomics features per patient, we trained, validated, and tested 4 machine-learning classifiers, to investigate the effect of no harmonization (NoH) or 4 feature harmonization methods, paired with no oversampling (NoO) or 5 oversampling methods on subtype prediction. Model performance was evaluated using the average area under the ROC curve (AUROC) and G-mean via 5 times 5-fold cross-validations. Statistical comparisons of the combined models against baseline (NoH + NoO) were performed for each fold of cross-validation using the DeLong test. Results The number of cross-combinations with both AUROC and G-mean outperforming baseline in validation and testing was 15, 4, 2, and 7 (out of 29) for random forest (RF), linear discriminant analysis (LDA), logistic regression (LR), and support vector machine (SVM), respectively. ComBat harmonization combined with oversampling (SMOTE) via RF yielded better performance than baseline (AUROC and G-mean of validation: 0.725 vs. 0.608 and 0.625 vs. 0.398; testing: 0.637 vs. 0.567 and 0.506 vs. 0.287), though statistical significances were not observed. Conclusions Applying harmonization and oversampling methods in multi-center imbalanced datasets can improve NSCLC-subtype prediction, but the effect varies widely across classifiers. We have created open-source comparisons of harmonization and oversampling on different classifiers for comprehensive evaluations in different studies.https://doi.org/10.1186/s40658-025-00750-7PET radiomicsHarmonizationOversamplingMulti-center imbalanced datasetsNSCLC |
| spellingShingle | Dongyang Du Isaac Shiri Fereshteh Yousefirizi Mohammad R. Salmanpour Jieqin Lv Huiqin Wu Wentao Zhu Habib Zaidi Lijun Lu Arman Rahmim Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes EJNMMI Physics PET radiomics Harmonization Oversampling Multi-center imbalanced datasets NSCLC |
| title | Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes |
| title_full | Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes |
| title_fullStr | Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes |
| title_full_unstemmed | Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes |
| title_short | Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes |
| title_sort | impact of harmonization and oversampling methods on radiomics analysis of multi center imbalanced datasets application to pet based prediction of lung cancer subtypes |
| topic | PET radiomics Harmonization Oversampling Multi-center imbalanced datasets NSCLC |
| url | https://doi.org/10.1186/s40658-025-00750-7 |
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