Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

Shan-Shan Yang; Xiong-Gang Yang; Xiao-Hong Yang; Xiao-Hua Hu

doi:10.1016/j.heliyon.2024.e30860

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

Heliyon. 2024 May 9;10(10):e30860. doi: 10.1016/j.heliyon.2024.e30860. eCollection 2024 May 30.

Authors

Shan-Shan Yang¹, Xiong-Gang Yang², Xiao-Hong Yang¹, Xiao-Hua Hu³

Affiliations

¹ Department of Prosthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi Medical University, Zunyi, China.
² Department of Orthopaedics, The First People's Hospital of Yunnan Province, Affiliated Hospital of Kunming University of Science and Technology, Kunming 650032, China.
³ Department of Oral and Maxillofacial Surgery, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi Medical University, Zunyi, China.

Abstract

Background: Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability.

Materials and methods: Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival.

Results: A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71-0.84; test: 0.77, 95%CI 0.68-0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates.

Conclusions: Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Keywords: Nomogram; Overall survival; Prognostic factors; SEER database; Submandibular gland cancer.