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European Radiology

03-04-2024 | Head and Neck

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland

Authors: Yanping He, Bowen Zheng, Weiwei Peng, Yongyu Chen, Lihui Yu, Weijun Huang, Genggeng Qin

Published in: European Radiology

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Abstract

Objectives

The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.

Methods

A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong’s test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.

Results

The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774–0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720–0.932) and clinical (0.814; 95% CI, 0.682–0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.

Conclusions

The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.

Clinical relevance statement

This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.

Key Points

• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy.
• Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy.
• The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.
Appendix
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Metadata
Title
An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland
Authors
Yanping He
Bowen Zheng
Weiwei Peng
Yongyu Chen
Lihui Yu
Weijun Huang
Genggeng Qin
Publication date
03-04-2024
Publisher
Springer Berlin Heidelberg
Published in
European Radiology
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-024-10719-2