Prognostic risk factor of major salivary gland carcinomas and survival prediction model based on random survival forests

Abstract

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.

Machine learning-based survival prediction nomogram for postoperative parotid mucoepidermoid carcinoma

“…ML techniques can capture nonlinear relationships and conditional dependencies in data, demonstrating high accuracy and flexibility when handling clinical research data. To date, several ML-based predictive models have been developed, including models based on conditional survival forests and random survival forests for the study of advanced salivary gland cancer and primary salivary gland cancer, respectively 18 , 19 .The determination of prognostic factors exhibits considerable variation among patients with P-MEC, largely due to differences in pathological grading and disease stage. Furthermore, the establishment of reliable prognostic factors remains a challenging task due to limitations such as patient size and subjective grading.…”

Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models

“…RSF employs a collection of decision trees and is particularly well-suited for complex survival analysis tasks. It brings improvements to the analysis of censored data and yields more accurate results in survival analysis problems [ 26 ].…”

Mutational Signatures in Salivary Gland Carcinomas

“…Mucoepidermoid (MEC), adenoid cystic (ADCC) and salivary duct (SDC) carcinomas represent the most significant sub-categories. MECs are mainly well-and moderately-differentiated carcinomas derived predominantly from the parotid glands (7). ADCC is the second most frequently detected carcinoma in all major glands, mainly being well differentiated, but also being characterized by elevated rates of recurrence after surgical excision and radiation-based treatment (8).…”