Objective:To analyze the risk factors affecting regional lymph node metastasis in salivary gland mucoepidermoid carcinoma (MEC) and to establish a nomogram model for individually predicting lymph node metastasis in salivary gland MEC.Methods:The clinical data of 2 152 patients with salivary gland MEC from 1975 to 2020 were collected from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute. The collected data were divided into training cohort (1 506 cases) and validation cohort (646 cases) according to the ratio of 7∶3. Single-factor regression and multi-factor logistic regression were used to screen factors related to local lymph node metastasis in salivary gland MEC, with constructing of a nomogram. Calibration curve, receiver operating characteristic (ROC) curve, area under the ROC curve (AUC) and decision curve analysis were used to evaluate model performance in the validation cohort and the total cohort. Statistical tests were performed using SPSS (26.0) and R (4.3.0) software.Results:Multivariate logistic regression results showed that M stage [ OR(95% CI):12.360(3.295-46.365), P=0.014], pathological grade Ⅱ、Ⅲ、Ⅳ[ OR(95% CI): 1.956(1.329-2.879), 9.654(6.309-14.772), 9.298(6.072-14.238), P<0.001], T staging T2, T3, T4[ OR(95% CI): 1.706(0.932-3.124), 3.021(1.790-5.096), 3.311(1.925-5.695), P<0.001], and gender [ OR(95% CI):0.759(0.593-0.972), P=0.029] were independent factors affecting local lymph node metastasis in salivary gland MEC. Through verification in the validation cohort and the total cohort, the AUC values were greater than 0.8, and the calibration curve was close to the perfect reference line, proving that the constructed nomogram model had good specificity and sensitivity for predicting local lymph node metastasis in salivary gland MEC. Conclusion:M stage, pathological grade, T stage, and gender are risk factors for predicting regional lymph node metastasis and the established-nomogram has good predictive performance for local lymph node metastasis in salivary gland MEC.

Objective:To establish and validate a radiomics nomogram based on MR for predicting cervical lymph node metastasis in laryngeal cancer.Methods:One hundred and seventeen patients with laryngeal cancer who underwent MR examinations and received open surgery and neck dissection between January 2016 and December 2019 were included in this study. All patients were randomly divided into a training cohort ( n=89) and test cohort ( n=28) using computer-generated random numbers. Clinical characteristics and MR were collected. Radiological features were extracted from the MR images. Enhanced T1 and T2WI were selected for radiomics analysis, and the volume of interest was manually segmented from the Huiyihuiying radiomics cloud platform. The variance analysis (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to reduce the dimensionality of the radiomics features in the training cohort. Then, a radiomic signature was established. The clinical risk factors were screened by using ANOVA and multivariate logistic regression. A nomogram was generated using clinical risk factors and the radiomic signature. The calibration curve and receiver operator characteristic (ROC) curve were used to confirm the nomogram′s performance in the training and test sets. The clinical usefulness of the nomogram was evaluated by decision curve analysis (DCA). Furthermore, a testing cohort was used to validate the model. Results:The radiomics signature consisted of 21 features, and the nomogram model included the radiomics signature and the MR-reported lymph node status. The model showed good calibration and discrimination. The model yielded areas under the ROC curve (AUC) in the training cohort, specificity, and sensitivity of 0.930, 0.930 and 0.875. In the test cohort, the model yielded AUC, specificity and sensitivity of 0.883, 0.889 and 0.800. DCA indicated that the nomogram model was clinically useful.Conclusion:The MR-based radiomics nomogram model may be used to predict cervical lymph node metastasis of laryngeal cancer preoperatively. MR-based radiomics could serve as a potential tool to help clinicians make an optimal clinical decision.