Development and validation of prognostic prediction model for submandibular gland cancer based on the SEER database

Junkun He; Feng Zhao; Jiangmiao Li; Qiyun Li; Fangyu Wei; Jiping Su

doi:10.1016/j.bjorl.2025.101654

Development and validation of prognostic prediction model for submandibular gland cancer based on the SEER database

Braz J Otorhinolaryngol. 2025 Jun 25;91(5):101654. doi: 10.1016/j.bjorl.2025.101654. Online ahead of print.

Authors

Junkun He¹, Feng Zhao¹, Jiangmiao Li¹, Qiyun Li¹, Fangyu Wei¹, Jiping Su²

Affiliations

¹ Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
² Department of Otolaryngology, Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. Electronic address: ymsu2@126.com.

PMID: 40570724
DOI: 10.1016/j.bjorl.2025.101654

Abstract

Objective: Accurately predicting the prognosis of Submandibular Gland Carcinoma (SGC) patients remains a challenging task. The purpose of this study was to develop a columnar graph prognostic prediction model for submandibular gland cancer based on the SEER database, using feature selection with lasso regression and modeling with Cox regression.

Methods: This study utilized data from the SEER database, focusing on 1362 cases of SGC. Various clinical and demographic factors, including age, tumor size, histology, and lymph node metastasis, were considered as potential prognostic factors. Feature selection was performed using lasso regression, and a Cox proportional hazards model was constructed, taking into account the complex interactions between variables and their impact on survival outcomes.

Results: The established prognostic prediction model demonstrated good accuracy and reliability. The model effectively identified several important prognostic factors, including age, tumor size, histology, and lymph node metastasis, which strongly influenced the prognosis of SGC. The model showed good discrimination and calibration with c-indexes of 0.802 (0.784‒0.821) in the training set and 0.756 (0.725‒0.787) in the validation set. The Decision Curve Analysis (DCA) curve reflected clinical utility.

Conclusion: This study suggests that the prognostic prediction model based on Cox regression is a valuable tool for predicting the prognosis of patients with SGC. This approach has the potential to improve patient outcomes by facilitating personalized treatment plans and identifying high-risk patients who may benefit from more aggressive interventions.

Level of evidence: Level III.

Keywords: Cox proportional hazards model; LASSO regression; Nomogram; SEER; Submandibular gland carcinoma.