Journal of stomatology, oral and maxillofacial surgery · 2025
A novel model for predicting prognosis in patients with metastatic major salivary gland carcinoma.
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Liu Shuyi, Wen Ziwen, Li Haodong, Geng Zhibao, Li Shifeng, Sun Xiaopeng, Bai Dan, Li Yu
Аннотация
BACKGROUND: Patients with metastatic major salivary gland carcinoma (SGCs) always end with a poor prognosis, and survival time is a major concern for clinicians and patients, but effective predictive tools are lacking in clinical practice.
METHODS: Clinical information on patients diagnosed with metastatic major SGCs was extracted from the SEER database. Cox analysis was applied to identify clinicopathological characteristics associated with patient overall survival (OS). A random survival forest (RSF) algorithm was used to establish an accurate prognostic prediction model for these patients.
RESULTS: Cox analysis revealed that age, T stage, N stage, pathology type, bone and liver metastasis, primary tumor surgery, chemotherapy, and radiotherapy were independent factors for OS among patients with metastatic major SGCs. Our RSF model has a C-index of 0.657 in the test set and 0.701 in the external validation set, and the area under the curve (AUC) values at 1, 3, and 5 years range from 0.715-0.802 in the test set and 0.655-0.918 in the external validation set. Patients were divided into high-risk and low-risk groups based on the risk score of the RSF model, and patients in the low-risk group had significantly better OS than those in the high-risk group, and chemotherapy did not benefit patients in the low-risk group.
CONCLUSION: In this study, a prognostic prediction model was constructed for patients with metastatic major SGCs using RSF algorithm, and the validation results indicate that the model has the potential to be a useful tool for clinicians in predicting survival and designing individualized treatment.
METHODS: Clinical information on patients diagnosed with metastatic major SGCs was extracted from the SEER database. Cox analysis was applied to identify clinicopathological characteristics associated with patient overall survival (OS). A random survival forest (RSF) algorithm was used to establish an accurate prognostic prediction model for these patients.
RESULTS: Cox analysis revealed that age, T stage, N stage, pathology type, bone and liver metastasis, primary tumor surgery, chemotherapy, and radiotherapy were independent factors for OS among patients with metastatic major SGCs. Our RSF model has a C-index of 0.657 in the test set and 0.701 in the external validation set, and the area under the curve (AUC) values at 1, 3, and 5 years range from 0.715-0.802 in the test set and 0.655-0.918 in the external validation set. Patients were divided into high-risk and low-risk groups based on the risk score of the RSF model, and patients in the low-risk group had significantly better OS than those in the high-risk group, and chemotherapy did not benefit patients in the low-risk group.
CONCLUSION: In this study, a prognostic prediction model was constructed for patients with metastatic major SGCs using RSF algorithm, and the validation results indicate that the model has the potential to be a useful tool for clinicians in predicting survival and designing individualized treatment.