Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer
Abstract Radiomic biomarkers offer promise for precision oncology. However, their clinical utility is limited by variability from differing imaging protocols and the high dimensionality of radiomics data. Feature selection is key for better interpretability, accuracy, and efficiency, yet traditional...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-12161-w |
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| author | Hajar Moradmand Jason Molitoris Xiao Ling Lisa Schumaker Erin Allor Hannah Thomas Danielle Arons Matthew Ferris Rebecca Krc William Silva Mendes Phuoc Tran Amit Sawant Ranee Mehra Daria A. Gaykalova Lei Ren |
| author_facet | Hajar Moradmand Jason Molitoris Xiao Ling Lisa Schumaker Erin Allor Hannah Thomas Danielle Arons Matthew Ferris Rebecca Krc William Silva Mendes Phuoc Tran Amit Sawant Ranee Mehra Daria A. Gaykalova Lei Ren |
| author_sort | Hajar Moradmand |
| collection | DOAJ |
| description | Abstract Radiomic biomarkers offer promise for precision oncology. However, their clinical utility is limited by variability from differing imaging protocols and the high dimensionality of radiomics data. Feature selection is key for better interpretability, accuracy, and efficiency, yet traditional methods lack stability and reproducibility. We investigate a Graph-Based Feature Selection (Graph-FS) approach that models feature interdependencies to identify stable radiomic signatures for head and neck squamous cell carcinoma (HNSCC) across institutions. We retrospectively analyzed 1,648 radiomic features extracted from the gross tumor volumes of 752 HNSCC patients from three institutions. After standard preprocessing and applying 36 radiomics parameter configurations to simulate variability, we compared Graph-FS with established methods: Boruta, Lasso, Recursive Feature Elimination (RFE), and Minimum Redundancy Maximum Relevance (mRMR). We evaluated feature selection stability and reproducibility using Pearson correlation, the Jaccard Index (JI), and the Dice-Sorensen Index (DSI) and assessed ranking consistency with Kendall’s Coefficient of Concordance (W). Graph-FS achieved higher stability (JI = 0.46, DSI = 0.62, OP = 45.8%) versus baseline methods with JI of 0.005 (Boruta), 0.010 (Lasso), 0.006 (RFE) and 0.014 (mRMR). These results demonstrate that Graph-FS enhances feature stability, reproducibility, and predictive performance. This method could facilitate integration into AI-driven radiomics workflows for reliable, multi-center biomarker discovery. |
| format | Article |
| id | doaj-art-2803fb99229f49758300bbce4b437c2a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2803fb99229f49758300bbce4b437c2a2025-08-20T03:46:08ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-12161-wGraph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancerHajar Moradmand0Jason Molitoris1Xiao Ling2Lisa Schumaker3Erin Allor4Hannah Thomas5Danielle Arons6Matthew Ferris7Rebecca Krc8William Silva Mendes9Phuoc Tran10Amit Sawant11Ranee Mehra12Daria A. Gaykalova13Lei Ren14Department of Radiation Oncology, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Mathematics, Auburn University at MontgomeryMarlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of MedicineInstitute for Genome Sciences, University of Maryland School of MedicineInstitute for Genome Sciences, University of Maryland School of MedicineInstitute for Genome Sciences, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineDepartment of Mathematics, Auburn University at MontgomeryMarlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland School of MedicineDepartment of Radiation Oncology, University of Maryland School of MedicineAbstract Radiomic biomarkers offer promise for precision oncology. However, their clinical utility is limited by variability from differing imaging protocols and the high dimensionality of radiomics data. Feature selection is key for better interpretability, accuracy, and efficiency, yet traditional methods lack stability and reproducibility. We investigate a Graph-Based Feature Selection (Graph-FS) approach that models feature interdependencies to identify stable radiomic signatures for head and neck squamous cell carcinoma (HNSCC) across institutions. We retrospectively analyzed 1,648 radiomic features extracted from the gross tumor volumes of 752 HNSCC patients from three institutions. After standard preprocessing and applying 36 radiomics parameter configurations to simulate variability, we compared Graph-FS with established methods: Boruta, Lasso, Recursive Feature Elimination (RFE), and Minimum Redundancy Maximum Relevance (mRMR). We evaluated feature selection stability and reproducibility using Pearson correlation, the Jaccard Index (JI), and the Dice-Sorensen Index (DSI) and assessed ranking consistency with Kendall’s Coefficient of Concordance (W). Graph-FS achieved higher stability (JI = 0.46, DSI = 0.62, OP = 45.8%) versus baseline methods with JI of 0.005 (Boruta), 0.010 (Lasso), 0.006 (RFE) and 0.014 (mRMR). These results demonstrate that Graph-FS enhances feature stability, reproducibility, and predictive performance. This method could facilitate integration into AI-driven radiomics workflows for reliable, multi-center biomarker discovery.https://doi.org/10.1038/s41598-025-12161-w |
| spellingShingle | Hajar Moradmand Jason Molitoris Xiao Ling Lisa Schumaker Erin Allor Hannah Thomas Danielle Arons Matthew Ferris Rebecca Krc William Silva Mendes Phuoc Tran Amit Sawant Ranee Mehra Daria A. Gaykalova Lei Ren Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer Scientific Reports |
| title | Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| title_full | Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| title_fullStr | Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| title_full_unstemmed | Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| title_short | Graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| title_sort | graph feature selection for enhancing radiomic stability and reproducibility across multiple institutions in head and neck cancer |
| url | https://doi.org/10.1038/s41598-025-12161-w |
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