BMC oral health · 2026
An integrated clinical and imaging-based machine learning model to determine the preoperative nature of primary operable parotid tumors.
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Du Danyi, Zhang Silin, Yang Chunping, Zhao Jiuzhou, Peng Lingyi, Liu Rong, Zheng Hairui, Jiang Haiwei, Qiu Chengyu, Li Rong, Zhang Xiangmin
Аннотация
BACKGROUND AND PURPOSE: The accurate preoperative classification of parotid tumors is crucial in selecting the appropriate treatment options. This study aimed to develop a machine learning model based on clinical, imaging, and laboratory test data to efficiently determine the preoperative nature of primary operable parotid tumors.
METHODS: A total of 779 patients with primary operable parotid tumors receiving surgical treatment from four independent clinical centers were enrolled. Standardized clinical, laboratory and multi-modality imaging indicators were extracted for model construction. We compared diagnostic performance from experienced clinicians' empirical diagnosis and ten machine learning algorithms via receiver operating characteristic (ROC) analysis on internal training/test cohorts and an independent external validation dataset.
RESULT: In total 779 enrolled subjects, empirical clinical prediction yielded an AUC(Area Under the Receiver Operating Characteristic Curve) of 0.83 (95% CI, 0.79-0.87), while the CatBoost model based on integrated multi-source data achieved an AUC of 0.91 (95% CI, 0.85-0.97). The CatBoost model demonstrated a sensitivity of 76%, specificity of 95%, Youden's index of 0.71, and accuracy of 92% for identifying malignant tumors.
CONCLUSION: The results of this study demonstrate that the CatBoost model constructed based on clinical, imaging and laboratory test features exhibits certain auxiliary predictive value in the preoperative assessment of primary operable parotid tumor nature, with its overall performance slightly superior to that of traditional clinical evaluation and logistic regression models.
METHODS: A total of 779 patients with primary operable parotid tumors receiving surgical treatment from four independent clinical centers were enrolled. Standardized clinical, laboratory and multi-modality imaging indicators were extracted for model construction. We compared diagnostic performance from experienced clinicians' empirical diagnosis and ten machine learning algorithms via receiver operating characteristic (ROC) analysis on internal training/test cohorts and an independent external validation dataset.
RESULT: In total 779 enrolled subjects, empirical clinical prediction yielded an AUC(Area Under the Receiver Operating Characteristic Curve) of 0.83 (95% CI, 0.79-0.87), while the CatBoost model based on integrated multi-source data achieved an AUC of 0.91 (95% CI, 0.85-0.97). The CatBoost model demonstrated a sensitivity of 76%, specificity of 95%, Youden's index of 0.71, and accuracy of 92% for identifying malignant tumors.
CONCLUSION: The results of this study demonstrate that the CatBoost model constructed based on clinical, imaging and laboratory test features exhibits certain auxiliary predictive value in the preoperative assessment of primary operable parotid tumor nature, with its overall performance slightly superior to that of traditional clinical evaluation and logistic regression models.