Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma
Abstract Uterine sarcomas are rare and aggressive tumors with heterogeneous outcomes, making accurate risk stratification crucial for personalized treatment. This study introduced a novel semi-supervised clustering approach, survival-guided adaptive Kmeans (SGAKmeans), for enhanced mortality risk st...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13139-4 |
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| author | Xue Zhou Suzhen Yuan Tianhui Li Tuao Zhang Wenwen Wang Xin Zhu |
| author_facet | Xue Zhou Suzhen Yuan Tianhui Li Tuao Zhang Wenwen Wang Xin Zhu |
| author_sort | Xue Zhou |
| collection | DOAJ |
| description | Abstract Uterine sarcomas are rare and aggressive tumors with heterogeneous outcomes, making accurate risk stratification crucial for personalized treatment. This study introduced a novel semi-supervised clustering approach, survival-guided adaptive Kmeans (SGAKmeans), for enhanced mortality risk stratification in early-stage uterine sarcoma patients. SGAKmeans uniquely integrated clinical characteristics and survival information, leveraging domain knowledge and soft pairwise constraints to adaptively adjust distance calculations. Using data from 1,836 uterine sarcoma patients in localized or regional stages in the SEER database, SGAKmeans identified three distinct risk groups with significantly different survival outcomes: high-risk (n = 293, 90.1% mortality, median survival 17 months), medium-risk (n = 767, 59.6% mortality, median survival 66 months), and low-risk (n = 776, 21.3% mortality). The method outperformed eight traditional clustering approaches in risk stratification performance and demonstrated robustness across various data distribution scenarios. Notably, the stratified groups showed differential responses to radiotherapy: high-risk patients benefited significantly (hazard ratio: 0.695, 95% CI 0.541–0.894), medium-risk patients showed no significant difference (0.910, 95% CI 0.747–1.110), while low-risk patients exhibited worse outcomes with radiotherapy (1.826, 95% CI 1.278–2.607). These findings highlighted the potential of SGAKmeans for more nuanced risk stratification and personalized treatment decisions in early-stage uterine sarcoma management, promoting precision medicine. |
| format | Article |
| id | doaj-art-e897586d836444f4a4bf366f75907d29 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e897586d836444f4a4bf366f75907d292025-08-20T03:05:17ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-13139-4Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcomaXue Zhou0Suzhen Yuan1Tianhui Li2Tuao Zhang3Wenwen Wang4Xin Zhu5Division of Information Systems, The University of AizuDepartment of Obstetrics and Gynecology, Tongji Hospital, Huazhong University of Science and TechnologyDivision of Information Systems, The University of AizuDivision of Information Systems, The University of AizuDepartment of Obstetrics and Gynecology, Tongji Hospital, Huazhong University of Science and TechnologyDepartment of AI Technology Development, M&D Data Medical Center, Institute of Integrated Research, Institute of Science TokyoAbstract Uterine sarcomas are rare and aggressive tumors with heterogeneous outcomes, making accurate risk stratification crucial for personalized treatment. This study introduced a novel semi-supervised clustering approach, survival-guided adaptive Kmeans (SGAKmeans), for enhanced mortality risk stratification in early-stage uterine sarcoma patients. SGAKmeans uniquely integrated clinical characteristics and survival information, leveraging domain knowledge and soft pairwise constraints to adaptively adjust distance calculations. Using data from 1,836 uterine sarcoma patients in localized or regional stages in the SEER database, SGAKmeans identified three distinct risk groups with significantly different survival outcomes: high-risk (n = 293, 90.1% mortality, median survival 17 months), medium-risk (n = 767, 59.6% mortality, median survival 66 months), and low-risk (n = 776, 21.3% mortality). The method outperformed eight traditional clustering approaches in risk stratification performance and demonstrated robustness across various data distribution scenarios. Notably, the stratified groups showed differential responses to radiotherapy: high-risk patients benefited significantly (hazard ratio: 0.695, 95% CI 0.541–0.894), medium-risk patients showed no significant difference (0.910, 95% CI 0.747–1.110), while low-risk patients exhibited worse outcomes with radiotherapy (1.826, 95% CI 1.278–2.607). These findings highlighted the potential of SGAKmeans for more nuanced risk stratification and personalized treatment decisions in early-stage uterine sarcoma management, promoting precision medicine.https://doi.org/10.1038/s41598-025-13139-4Uterine sarcomaRisk stratificationSemi-supervised clusteringAdjuvant radiotherapySurvival analysisPrognosis |
| spellingShingle | Xue Zhou Suzhen Yuan Tianhui Li Tuao Zhang Wenwen Wang Xin Zhu Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma Scientific Reports Uterine sarcoma Risk stratification Semi-supervised clustering Adjuvant radiotherapy Survival analysis Prognosis |
| title | Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| title_full | Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| title_fullStr | Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| title_full_unstemmed | Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| title_short | Survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| title_sort | survival guided adaptive clustering enhances mortality risk stratification and radiotherapy guidance in early stage uterine sarcoma |
| topic | Uterine sarcoma Risk stratification Semi-supervised clustering Adjuvant radiotherapy Survival analysis Prognosis |
| url | https://doi.org/10.1038/s41598-025-13139-4 |
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