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Objectives <p>Cervical lymph node (CLN) status is an important factor for the patients with major salivary gland carcinomas (MSGCs) with respect to the surgical methods, prognosis, and recurrence. Our aim is to develop a risk model that incorporates clinicopathological and ultrasound (US) features to predict the cervical lymph node metastasis (CLNM) in MSGCs.</p> Methods <p>Retrospective data were gathered for 111 patients with MSGCs who underwent surgical treatment and US examinations at our institution from January 2016 to December 2022. Their clinicopathological and US data were documented and analyzed. Independent predictors predicting CLNM in MSGCs were screened through univariate and multivariate analysis. The nomogram model were built based on independent predictors using logistic regression. The evaluation of the model's performance was then conducted.</p> Results <p>The clinicopathological and US factors of patient age, lesion size, US reported CLN-positive, histological type, and histological grade were identified as independent predictors for predicting CLNM in MSGCs. The nomogram model, which integrated these predictive factors, achieved an AUC of 0.923 (95% CI: 0.869 ~ 0.977), demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curve further confirmed its clinical usefulness.</p> Conclusions <p>The nomogram model we developed holds the potential to predict CLNM in MSGCs preoperatively, thereby enabling the provision of more precise therapeutic strategies.</p>

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Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Huan-Zhong Su,
  • Ji-Chao Lin,
  • Long-Cheng Hong,
  • Yu-Hui Wu,
  • Feng Zhang,
  • Kun Yu,
  • Xiao-Dong Zhang,
  • Zuo-Bing Zhang

摘要

Objectives

Cervical lymph node (CLN) status is an important factor for the patients with major salivary gland carcinomas (MSGCs) with respect to the surgical methods, prognosis, and recurrence. Our aim is to develop a risk model that incorporates clinicopathological and ultrasound (US) features to predict the cervical lymph node metastasis (CLNM) in MSGCs.

Methods

Retrospective data were gathered for 111 patients with MSGCs who underwent surgical treatment and US examinations at our institution from January 2016 to December 2022. Their clinicopathological and US data were documented and analyzed. Independent predictors predicting CLNM in MSGCs were screened through univariate and multivariate analysis. The nomogram model were built based on independent predictors using logistic regression. The evaluation of the model's performance was then conducted.

Results

The clinicopathological and US factors of patient age, lesion size, US reported CLN-positive, histological type, and histological grade were identified as independent predictors for predicting CLNM in MSGCs. The nomogram model, which integrated these predictive factors, achieved an AUC of 0.923 (95% CI: 0.869 ~ 0.977), demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curve further confirmed its clinical usefulness.

Conclusions

The nomogram model we developed holds the potential to predict CLNM in MSGCs preoperatively, thereby enabling the provision of more precise therapeutic strategies.