Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features
Abstract 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) feat...
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BMC
2025-07-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-06344-0 |
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| author | Huan-Zhong Su Ji-Chao Lin Long-Cheng Hong Yu-Hui Wu Feng Zhang Kun Yu Xiao-Dong Zhang Zuo-Bing Zhang |
| author_facet | Huan-Zhong Su Ji-Chao Lin Long-Cheng Hong Yu-Hui Wu Feng Zhang Kun Yu Xiao-Dong Zhang Zuo-Bing Zhang |
| author_sort | Huan-Zhong Su |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c63c33d20e414afa85fa7dac66a65c05 |
| institution | Kabale University |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-c63c33d20e414afa85fa7dac66a65c052025-08-20T03:45:38ZengBMCBMC Oral Health1472-68312025-07-0125111210.1186/s12903-025-06344-0Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound featuresHuan-Zhong Su0Ji-Chao Lin1Long-Cheng Hong2Yu-Hui Wu3Feng Zhang4Kun Yu5Xiao-Dong Zhang6Zuo-Bing Zhang7Department of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Stomatology, Zhongshan Hospital (Xiamen), Fudan UniversityDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Pathology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityDepartment of Ultrasound, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen UniversityAbstract 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.https://doi.org/10.1186/s12903-025-06344-0Major salivary gland carcinomaCervical lymph node metastasisUltrasoundRisk model |
| spellingShingle | Huan-Zhong Su Ji-Chao Lin Long-Cheng Hong Yu-Hui Wu Feng Zhang Kun Yu Xiao-Dong Zhang Zuo-Bing Zhang Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features BMC Oral Health Major salivary gland carcinoma Cervical lymph node metastasis Ultrasound Risk model |
| title | Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| title_full | Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| title_fullStr | Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| title_full_unstemmed | Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| title_short | Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| title_sort | development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features |
| topic | Major salivary gland carcinoma Cervical lymph node metastasis Ultrasound Risk model |
| url | https://doi.org/10.1186/s12903-025-06344-0 |
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