Naderian Jahromi, S.; Raoufi, S.; Rahimi, M.; Bazmandeh, D.; Salehi, A.; Saleh, K.; Tarokhian, A. Machine Learning-Based Mortality Risk Stratification in Synovial Sarcoma Patients: A Retrospective Study. Preprints2024, 2024091326. https://doi.org/10.20944/preprints202409.1326.v1
APA Style
Naderian Jahromi, S., Raoufi, S., Rahimi, M., Bazmandeh, D., Salehi, A., Saleh, K., & Tarokhian, A. (2024). Machine Learning-Based Mortality Risk Stratification in Synovial Sarcoma Patients: A Retrospective Study. Preprints. https://doi.org/10.20944/preprints202409.1326.v1
Chicago/Turabian Style
Naderian Jahromi, S., Kianmehr Saleh and Aidin Tarokhian. 2024 "Machine Learning-Based Mortality Risk Stratification in Synovial Sarcoma Patients: A Retrospective Study" Preprints. https://doi.org/10.20944/preprints202409.1326.v1
Abstract
Background: Synovial sarcoma is a rare soft tissue tumor that constitutes 5-10% of all soft tissue sarcomas. Early diagnosis and risk stratification are essential for effective management. This study leverages machine learning models to improve the risk stratification of mortality in synovial sar-coma patients.
Methods: This retrospective cohort study utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to analyze patients diagnosed with synovial sarcoma between 2004 and 2015, as well as a validation cohort diagnosed from 2018 onward. The dataset encompassed demographic data, clinical characteristics, staging, treatment modalities, and outcomes. Four ma-chine learning models—support vector classifier (SVC), k-nearest neighbors (KNN), Gaussian naive Bayes, and gradient boosting—were trained and evaluated. Model performance was assessed using sensitivity, specificity, AUC-ROC, and Brier score. SHAP analysis was performed to determine the most influential features impacting model predictions. The top-performing model was validated using the 2018+ cohort.
Results: The study included a total of 762 patients, with an average age of 39.4 years. The support vector classifier (SVC) outperformed the other models, achieving an AUC-ROC of 0.8153 (95% CI: 0.7578 to 0.8677) and a Brier score of 0.1715. Upon external validation with the 2018+ cohort, the SVC model yielded an AUC-ROC of 0.8179 (95% CI: 0.7632 to 0.8666). Key prognostic factors identified through SHAP analysis included tumor size, patient age, presence of metastasis, tumor differentiation, and cancer stage.
Conclusion: Machine learning models can be used to stratify the risk of death in synovial sarcoma patients effectively, with the support vector classifier showing the most promise.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
