ABSTRACT Objective To construct a column-line diagram model based on ultrasound imaging histology, ultrasound image features and clinical data, and to explore its clinical value for preoperative prediction of lymph node metastasis in the lateral neck region (LNLN) in patients with papillary thyroid carcinoma (PTC). Methods 161 patients with PTC confirmed by surgical pathology 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 whom had complete ultrasonographic 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 to outline and extract the imaging histology features in the region of interest (ROI), 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 imaging histology score (RS) of each lesion was calculated. Logistic regression analysis was used to screen the clinical data, ultrasound image features and ultrasound imaging histology features, and the clinical model, ultrasound image features model, ultrasound imaging histology model and joint model were constructed, respectively. The efficacy of each model in predicting PTC LNLN transfer was analyzed by plotting the subject operating characteristic (ROC) curve. Calibration curves were applied to assess the calibration of each model.Results Univariate and multivariate logistic regression analyses showed that gender (OR=1.117) and tumor maximum diameter (OR=2.935) were independent influences on LNLN metastasis, and clinical models and ultrasound image feature models were constructed based on the above 2 independent influences, respectively. A total of 6 ultrasound image histologic features with non-zero coefficients were screened by LASSO regression downscaling for the construction of the ultrasound image histologic model. The RS of the LNLN metastasis-positive and negative groups in the training set were (0.51±0.25) and (0.22±0.19), respectively; and that of the LNLN metastasis-positive and negative groups in the validation set were (0.68±0.28) and (0.44±0.23), respectively, and the differences were statistically significant (both P<0.05).The analysis of the ROC curves showed that the clinical model predicted the LNLN metastasis-positive and negative groups in the training and validation sets. set and validation set, the area under the curve (AUC) of the clinical model for predicting LNLN metastasis in the positive and negative groups were 0.635 and 0.538, respectively, and the AUCs of the ultrasound image characterization model were 0.757 and 0.741, respectively, the AUCs of the ultrasound imaging histology model were 0.824 and 0.747, respectively, and the AUCs of the combined model were 0.843 and 0.778, respectively; The AUCs of the ultrasound imaging histology model and the combined model were higher than those of the clinical model, and the differences were statistically significant (P < 0.05). The calibration curves showed that both the ultrasound imaging histology model and the joint model were highly calibrated, and both were in good agreement with the actual results.Conclusion Constructing a nomogram model based on ultrasound imaging histology, ultrasound image features and clinical data for preoperative prediction of LNLN metastasis in PTC patients has important clinical value.