Clinical value of nomogram model based on ultrasound radiomics in predicting lymph node metastasis in the lateral neck region in papillary thyroid carcinoma
Clinical value of nomogram model based on ultrasound radiomics in predicting lymph node metastasis in the lateral neck region in papillary thyroid carcinoma
Objective To construct a nomogram model based on ultrasound radiomics,ultrasound image features and clinical data,and to explore its clinical value in predicting lateral neck lymph node(LNLN)metastasis in patients with papillary thyroid carcinoma(PTC).Methods A total of 161 patients with PTC confirmed by surgical pathology in our hospital were selected and randomly divided into 112 cases in the training set and 49 cases in the validation set according to the ratio of 7∶3,all of them had complete ultrasonic and clinical data and were divided into 50 cases in the LNLN metastasis-positive group and 111 cases in the LNLN metastasis-negative group according to the pathological results.Based on the gray-scale ultrasound images of the training set,the region of interest were delineated and the radiomics features were extracted.The least absolute shrinkage and selection operator(LASSO)algorithm was used to screen the features related to LNLN metastasis in patients with PTC,and the rad-score(RS)was calculated.Univariate and multivariate Logistic regression analysis was used to screen the independent influencing factors from clinical data and ultrasound image features for LNLN metastasis in PTC patients.The clinical model,ultrasound image features model,ultrasound radiomics model and combined model of the three were constructed,respectively.The efficacy of each model in predicting LNLN metastasis in PTC patients was analyzed by receiver operating characteristic(ROC)curve.Calibration curve was applied to assess the calibration of each model.Results Univariate and multivariate Logistic regression analysis showed that gender and tumor maximum diameter were independent influencing factor for LNLN metastasis(OR=3.167,1.177,both P<0.05).A total of 6 ultrasound radiomics features with non-zero coefficients were screened by LASSO regression downscaling.The RS of the LNLN metastasis-positive and negative groups in the training set were(0.51±0.25)points and(0.22±0.19)points,respectively,and that of the LNLN metastasis-positive and negative groups in the validation set were(0.68±0.28)points and(0.44±0.23)points,respectively.The differences in RS between the two groups were statistically significant in both sets(both P<0.05).Clinical models,ultrasound image feature models and ultrasound radiomics models were constructed based on gender,the maximum tumor diameter and RS,respectively.A combined model was constructed based on the combination of above three and visualized by drawing a nomogram.ROC curve analysis showed that in the training and validation sets,the area under the curve(AUC)of the clinical model for predicting LNLN metastasis in PTC patients were 0.635 and 0.538,respectively,and the AUC of the ultrasound image features model were 0.757 and 0.741,respectively,the AUC of the ultrasound radiomics model were 0.824 and 0.747,respectively,and the AUC of the combined model were 0.843 and 0.778,respectively.The AUC of the combined model was highest,and the differences were statistically significant(all P<0.05).Calibration curve demonstrated that the calibration degrees of both the ultrasound radiomics model and the combined model were relatively high,and the consistency between the predicted probabilities and the actual probabilities was satisfactory.Conclusion The nomogram model constructed based on ultrasound radiomics,ultrasound image features and clinical data has important clinical value in predicting LNLN metastasis in patients with PTC.
维普
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