Objective:To explore the efficacy of the features of ultrasound imaging omics at the area peripheral thyroid nodule in predicting cervical lymph node metastasis(LNM)of papillary thyroid carcinoma(PTC),and construct a prediction model based on the features of imaging omics and to verify its performance.Methods:A total of 237 PTC patients who admitted to Shaanxi Provincial Hospital of Chinese Medicine from March 2021 to June 2024 and were confirmed by pathology were retrospectively collected.They were divided into the training set(166 cases)and the validation set(71 cases)as a ratio of 7 to 3.According to the postoperatively pathological results,237 patients were divided into the metastasis group(108 cases)and the non-metastasis group(129 cases).The clinical data and conventional ultrasound characteristic information of all patients were collected,and a feature model of imaging omics was constructed through quantitative extracting and screening the features of ultrasound imaging omics within nodules and peripheral nodules,and utilizing machine learning classifier.Then,the feature score(Rad-Score)of image omics was obtained.The Rad-Score values within and peripheral nodules,and the Rad-Score values peripheral nodules of metastasis group and non-metastasis group were compared.In training set,the independent risk factors of affecting neck LNM were analyzed,and a clinical-ultrasonic model was constructed,which was combined with Rad-Score to construct a joint model based on the features of imaging omics peripheral nodules.The receiver operating characteristic(ROC)curve was used to analyze and compare its predictive efficacy.The nomogram of the joint model was constructed,and then,the calibration and fitting degrees of the nomogram were assessed by calibration curve and Hosmer-Lemeshow test.Result:In training set,6 features of imaging omics within nodules and 11 features of imaging omics peripheral nodules were respectively extracted and screened out through the least absolute shrinkage and selection operator(LASSO)algorithm.In the training set and validation set,the Rad-Scores peripheral the nodules in the metastasis group were respectively(7.43±0.45)points and(7.19±0.51)points,which were significantly higher than(3.25±0.28)points and(3.51±0.32)points peripheral the nodules in the non-metastasis group,and the differences were statistically significant(t=72.708,61.222,P<0.05).The results of factor analysis showed that age,capsule invasion,microcalcification,ultrasound-indicated lymph node positivity and Rad-Score around nodules were independent risk factors of affecting cervical LNM of PTC patients(OR=0.592,2.983,3.593,4.424,2.575,P<0.05).The ROC curve showed that the area under curve(AUC)values of the ROC curve of joint model in training set and validation set were respectively 0.861 and 0.872 in predicting LNM,respectively,which were superior to 0.759 and 0.783 of the clinical-ultrasound model.Conclusion:In both the training set and the validation set,the nomogram of joint model has favorable calibration and fitting in predicting cervical LNM of PTC patients.The construction of clinical model based on the features of ultrasound imaging omics peripheral nodules has a favorable efficacy in predicting the status of cervical lymph node of PTC patients before surgery,which is expected to be an effective tool of individual prediction for LNM.

Objective:To explore the efficacy of the features of ultrasound imaging omics at the area peripheral thyroid nodule in predicting cervical lymph node metastasis(LNM)of papillary thyroid carcinoma(PTC),and construct a prediction model based on the features of imaging omics and to verify its performance.Methods:A total of 237 PTC patients who admitted to Shaanxi Provincial Hospital of Chinese Medicine from March 2021 to June 2024 and were confirmed by pathology were retrospectively collected.They were divided into the training set(166 cases)and the validation set(71 cases)as a ratio of 7 to 3.According to the postoperatively pathological results,237 patients were divided into the metastasis group(108 cases)and the non-metastasis group(129 cases).The clinical data and conventional ultrasound characteristic information of all patients were collected,and a feature model of imaging omics was constructed through quantitative extracting and screening the features of ultrasound imaging omics within nodules and peripheral nodules,and utilizing machine learning classifier.Then,the feature score(Rad-Score)of image omics was obtained.The Rad-Score values within and peripheral nodules,and the Rad-Score values peripheral nodules of metastasis group and non-metastasis group were compared.In training set,the independent risk factors of affecting neck LNM were analyzed,and a clinical-ultrasonic model was constructed,which was combined with Rad-Score to construct a joint model based on the features of imaging omics peripheral nodules.The receiver operating characteristic(ROC)curve was used to analyze and compare its predictive efficacy.The nomogram of the joint model was constructed,and then,the calibration and fitting degrees of the nomogram were assessed by calibration curve and Hosmer-Lemeshow test.Result:In training set,6 features of imaging omics within nodules and 11 features of imaging omics peripheral nodules were respectively extracted and screened out through the least absolute shrinkage and selection operator(LASSO)algorithm.In the training set and validation set,the Rad-Scores peripheral the nodules in the metastasis group were respectively(7.43±0.45)points and(7.19±0.51)points,which were significantly higher than(3.25±0.28)points and(3.51±0.32)points peripheral the nodules in the non-metastasis group,and the differences were statistically significant(t=72.708,61.222,P<0.05).The results of factor analysis showed that age,capsule invasion,microcalcification,ultrasound-indicated lymph node positivity and Rad-Score around nodules were independent risk factors of affecting cervical LNM of PTC patients(OR=0.592,2.983,3.593,4.424,2.575,P<0.05).The ROC curve showed that the area under curve(AUC)values of the ROC curve of joint model in training set and validation set were respectively 0.861 and 0.872 in predicting LNM,respectively,which were superior to 0.759 and 0.783 of the clinical-ultrasound model.Conclusion:In both the training set and the validation set,the nomogram of joint model has favorable calibration and fitting in predicting cervical LNM of PTC patients.The construction of clinical model based on the features of ultrasound imaging omics peripheral nodules has a favorable efficacy in predicting the status of cervical lymph node of PTC patients before surgery,which is expected to be an effective tool of individual prediction for LNM.