In Cancer medicine
Salivary gland malignancies are rare and are often acompanied by poor prognoses. So, identifying the populations with risk factors and timely intervention to avoid disease progression is significant. This study provides an effective prediction model to screen the target patients and is helpful to construct a cost-effective follow-up strategy. We enrolled 249 patients diagnosed with salivary gland tumors and analyzed prognostic risk factors using Cox proportional hazard univariable and multivariable regression models. The patients' data were split into training and validation sets on a 7:3 ratio, and the random survival forest (RSF) model was established using the training sets and validated using the validation sets. The maximally selected rank statistics method was used to determine a cut point value corresponding to the most significant relation with survival. Univariable Cox regression suggested age, smoking, alcohol consumption, untreated, neural invasion, capsular invasion, skin invasion, tumors larger than 4 cm, advanced T and N stage, distant metastasis, and non-mucous cell carcinoma were risk factors for poor prognosis, and multivariable analysis suggested that female, aging, smoking, untreated, and non-mucous cell carcinoma were risk factors. The time-dependent ROC curve showed the AUC of the RSF prediction model on 1-, 2-, and 3-year survival were 0.696, 0.779, and 0.765 respectively in the validation sets. Log-rank tests suggested that the cut point 7.42 risk score calculated from the RSF was most effective in dividing patients with significantly different prognoses. The prediction model based on the RSF could effectively screen patients with poor prognoses.
Chen Yufan, Li Guoli, Jiang Wenmei, Nie Rong Cheng, Deng Honghao, Chen Yingle, Li Hao, Chen Yanfeng
2023-Mar-19
machine learning, major salivary gland tumors, prediction model, prognosis, random survival forest
