SNUH methylation classifier for CNS tumors
Abstract Background Methylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and i...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-03-01
|
| Series: | Clinical Epigenetics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13148-025-01824-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850039469226328064 |
|---|---|
| author | Kwanghoon Lee Jaemin Jeon Jin Woo Park Suwan Yu Jae-Kyung Won Kwangsoo Kim Chul-Kee Park Sung-Hye Park |
| author_facet | Kwanghoon Lee Jaemin Jeon Jin Woo Park Suwan Yu Jae-Kyung Won Kwangsoo Kim Chul-Kee Park Sung-Hye Park |
| author_sort | Kwanghoon Lee |
| collection | DOAJ |
| description | Abstract Background Methylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and innovative machine-learning techniques. Results Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. For ‘Filtered Test Data Set 1,’ the SNUH-MC achieved higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4 (F1-micro: 0.907, F1-macro: 0.627). We evaluated the performance of three classifiers; SNUH-MC, DKFZ-MC v11b4, and DKFZ-MC v12.5, using specific criteria. We set established ‘Decisions’ categories based on histopathology, clinical information, and next-generation sequencing to assess the classification results. When applied to 193 unknown SNUH methylation data samples, SNUH-MC notably improved diagnosis compared to DKFZ-MC v11b4. Specifically, 17 cases were reclassified as ‘Match’ and 34 cases as ‘Likely Match’ when transitioning from DKFZ-MC v11b4 to SNUH-MC. Additionally, SNUH-MC demonstrated similar results to DKFZ-MC v12.5 for 23 cases that were unclassified by v11b4. Conclusions This study presents SNUH-MC, an innovative methylation-based classification tool that significantly advances the field of neuropathology and bioinformatics. Our classifier incorporates cutting-edge techniques such as the SMOTE and OpenMax resulting in improved diagnostic accuracy and robustness, particularly when dealing with unknown or noisy data. |
| format | Article |
| id | doaj-art-2e6a0d8ccaf342dbbdfd8c54fe024f2a |
| institution | DOAJ |
| issn | 1868-7083 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | BMC |
| record_format | Article |
| series | Clinical Epigenetics |
| spelling | doaj-art-2e6a0d8ccaf342dbbdfd8c54fe024f2a2025-08-20T02:56:20ZengBMCClinical Epigenetics1868-70832025-03-0117111910.1186/s13148-025-01824-0SNUH methylation classifier for CNS tumorsKwanghoon Lee0Jaemin Jeon1Jin Woo Park2Suwan Yu3Jae-Kyung Won4Kwangsoo Kim5Chul-Kee Park6Sung-Hye Park7Department of Pathology, Seoul National University College of MedicineInterdisciplinary Program in Bioinformatics, Seoul National UniversityDepartment of Pathology, Yonsei University College of MedicineInterdisciplinary Program in Bioinformatics, Seoul National UniversityDepartment of Pathology, Seoul National University College of MedicineDepartment of Transdisciplinary Medicine, Seoul National University HospitalDepartment of Neurosurgery, Seoul National University College of MedicineDepartment of Pathology, Seoul National University College of MedicineAbstract Background Methylation profiling of central nervous system (CNS) tumors, pioneered by the German Cancer Research Center, has significantly improved diagnostic accuracy. This study aimed to further enhance the performance of methylation classifiers by leveraging publicly available data and innovative machine-learning techniques. Results Seoul National University Hospital Methylation Classifier (SNUH-MC) addressed data imbalance using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm and incorporated OpenMax within a Multi-Layer Perceptron to prevent labeling errors in low-confidence diagnoses. Compared to two published CNS tumor methylation classification models (DKFZ-MC: Deutsches Krebsforschungszentrum Methylation Classifier v11b4: RandomForest, 767-MC: Multi-Layer Perceptron), our SNUH-MC showed improved performance in F1-score. For ‘Filtered Test Data Set 1,’ the SNUH-MC achieved higher F1-micro (0.932) and F1-macro (0.919) scores compared to DKFZ-MC v11b4 (F1-micro: 0.907, F1-macro: 0.627). We evaluated the performance of three classifiers; SNUH-MC, DKFZ-MC v11b4, and DKFZ-MC v12.5, using specific criteria. We set established ‘Decisions’ categories based on histopathology, clinical information, and next-generation sequencing to assess the classification results. When applied to 193 unknown SNUH methylation data samples, SNUH-MC notably improved diagnosis compared to DKFZ-MC v11b4. Specifically, 17 cases were reclassified as ‘Match’ and 34 cases as ‘Likely Match’ when transitioning from DKFZ-MC v11b4 to SNUH-MC. Additionally, SNUH-MC demonstrated similar results to DKFZ-MC v12.5 for 23 cases that were unclassified by v11b4. Conclusions This study presents SNUH-MC, an innovative methylation-based classification tool that significantly advances the field of neuropathology and bioinformatics. Our classifier incorporates cutting-edge techniques such as the SMOTE and OpenMax resulting in improved diagnostic accuracy and robustness, particularly when dealing with unknown or noisy data.https://doi.org/10.1186/s13148-025-01824-0MethylationBrain tumorsClassificationNext-generation sequencingTargeted therapy |
| spellingShingle | Kwanghoon Lee Jaemin Jeon Jin Woo Park Suwan Yu Jae-Kyung Won Kwangsoo Kim Chul-Kee Park Sung-Hye Park SNUH methylation classifier for CNS tumors Clinical Epigenetics Methylation Brain tumors Classification Next-generation sequencing Targeted therapy |
| title | SNUH methylation classifier for CNS tumors |
| title_full | SNUH methylation classifier for CNS tumors |
| title_fullStr | SNUH methylation classifier for CNS tumors |
| title_full_unstemmed | SNUH methylation classifier for CNS tumors |
| title_short | SNUH methylation classifier for CNS tumors |
| title_sort | snuh methylation classifier for cns tumors |
| topic | Methylation Brain tumors Classification Next-generation sequencing Targeted therapy |
| url | https://doi.org/10.1186/s13148-025-01824-0 |
| work_keys_str_mv | AT kwanghoonlee snuhmethylationclassifierforcnstumors AT jaeminjeon snuhmethylationclassifierforcnstumors AT jinwoopark snuhmethylationclassifierforcnstumors AT suwanyu snuhmethylationclassifierforcnstumors AT jaekyungwon snuhmethylationclassifierforcnstumors AT kwangsookim snuhmethylationclassifierforcnstumors AT chulkeepark snuhmethylationclassifierforcnstumors AT sunghyepark snuhmethylationclassifierforcnstumors |