Objective:To investigate the correlation of color Doppler flow imaging (CDFI) and shear wave elastography (SWE) with pathological classification and prognosis of breast cancer patients.Methods:A total of 87 patients (103 lesions) with breast cancer admitted to Shanxi Maternal and Child Health Care Hospital and the Second Hospital of Shanxi Medical University From May. 2021 to Mar. 2024 were retrospectively included. All patients underwent CDFI and SWE examinations before surgery. The pathological characteristics and molecular typing of each lesion were recorded, and the correlation of CDFI and SWE examination parameters with molecular typing of breast cancer was evaluated. Patients were followed up for 1 year, and the predictive value of CDFI and SWE parameters in lymph node metastasis was analyzed by receiver operating characteristic curve (ROC) .Results:There were no statistically significant differences in the pulse index (PI) , resistance index (RI) , maximum lesion elastic modulus (E max) , and the ratio between the elastic value at the hardest lesion and the elastic value of adipose tissue (E ratio) among patients with different pathological types ( F=0.64, 0.13, 0.81, 2.84, P>0.05) . There were no statistically significant differences in PI and RI values among patients with different tumor sizes ( F=2.99, 1.81, P>0.05) , and statistically significant differences in E max and E ratio among patients with different tumor sizes ( F=6.42, 34.31, P<0.05) . The differences among different molecular types PI, RI, E max, and E ratio were statistically significant ( F=406.59, 245.23, 206.30, 204.36, P<0.05) , and Luminal B type PI, RI, E max, and E ratio were the highest, followed by HER2-positive, triple-negative, and Luminal A type, with statistically significant differences ( P<0.05) . PI, RI, E max and E ratio in patients with positive lymph node metastasis were higher than those in patients with negative lymph node metastasis ( t=4.99, 3.04, 2.70, 3.13, all P<0.05) . ROC results showed that the area under the curve (AUC) of PI, RI, E max and E ratio for predicting lymph node metastasis of breast cancer were 0.654, 0.704, 0.664 and 0.696, respectively. The sensitivity to predict lymph node metastasis of breast cancer was 74.19%, 54.84%, 51.61%, 64.52, and the specificity was 54.17%, 79.17%, 79.17%, 70.83% (all P<0.05) . Conclusions:The correlation of CDFI and SWE examination parameters are correlated with the molecular classification of breast cancer, and the prediction of lymph node metastasis of breast cancer is good.

Objective:To investigate the correlation of color Doppler flow imaging (CDFI) and shear wave elastography (SWE) with pathological classification and prognosis of breast cancer patients.Methods:A total of 87 patients (103 lesions) with breast cancer admitted to Shanxi Maternal and Child Health Care Hospital and the Second Hospital of Shanxi Medical University From May. 2021 to Mar. 2024 were retrospectively included. All patients underwent CDFI and SWE examinations before surgery. The pathological characteristics and molecular typing of each lesion were recorded, and the correlation of CDFI and SWE examination parameters with molecular typing of breast cancer was evaluated. Patients were followed up for 1 year, and the predictive value of CDFI and SWE parameters in lymph node metastasis was analyzed by receiver operating characteristic curve (ROC) .Results:There were no statistically significant differences in the pulse index (PI) , resistance index (RI) , maximum lesion elastic modulus (E max) , and the ratio between the elastic value at the hardest lesion and the elastic value of adipose tissue (E ratio) among patients with different pathological types ( F=0.64, 0.13, 0.81, 2.84, P>0.05) . There were no statistically significant differences in PI and RI values among patients with different tumor sizes ( F=2.99, 1.81, P>0.05) , and statistically significant differences in E max and E ratio among patients with different tumor sizes ( F=6.42, 34.31, P<0.05) . The differences among different molecular types PI, RI, E max, and E ratio were statistically significant ( F=406.59, 245.23, 206.30, 204.36, P<0.05) , and Luminal B type PI, RI, E max, and E ratio were the highest, followed by HER2-positive, triple-negative, and Luminal A type, with statistically significant differences ( P<0.05) . PI, RI, E max and E ratio in patients with positive lymph node metastasis were higher than those in patients with negative lymph node metastasis ( t=4.99, 3.04, 2.70, 3.13, all P<0.05) . ROC results showed that the area under the curve (AUC) of PI, RI, E max and E ratio for predicting lymph node metastasis of breast cancer were 0.654, 0.704, 0.664 and 0.696, respectively. The sensitivity to predict lymph node metastasis of breast cancer was 74.19%, 54.84%, 51.61%, 64.52, and the specificity was 54.17%, 79.17%, 79.17%, 70.83% (all P<0.05) . Conclusions:The correlation of CDFI and SWE examination parameters are correlated with the molecular classification of breast cancer, and the prediction of lymph node metastasis of breast cancer is good.

Objective To observe the efficacy of gray-scale ultrasound-based radiomics for differentiating peripheral pulmonary adenocarcinoma and squamous cell carcinoma.Methods Data of 88 patients with single peripheral lung adenocarcinoma and 58 patients with single peripheral lung squamous cell carcinoma proved pathologically with puncture biopsy and clearly visualized with lung ultrasound were retrospectively analyzed.The patients were divided into training set(n=103)and test set(n=43)at the ratio of 7:3.Based on gray-scale ultrasound of training set,radiomics features associated with differential diagnosis of peripheral lung adenocarcinoma and lung squamous cell carcinoma were extracted and screened.Using 4 different classifiers,including support vector machine(SVM),linear discriminant analysis(LDA),logistic regression(LR)and the least absolute shrinkage and selection operator combined with logistic regression(LASSO-LR),4 corresponding radiomics models were obtained,and the relative best models were selected according to their performances under 10-fold cross validation.The receiver operating characteristic curves were drawn,the areas under the curve(AUC)were calculated to evaluate the differentiating efficacy of each model,and DeLong test was used for the comparison.The differentiating accuracy of models were obtained under the best cutoff value with the maximum Youden index.Results The AUC of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous carcinoma in test set was 0.864,0.867,0.880 and 0.844,respectively,and no significant difference was found among 4 models(all P＞0.05).Under the best cutoff value of each model,the corresponding accuracy of SVM,LDA,LR and LASSO-LR radiomics models for differentiating peripheral lung adenocarcinoma and lung squamous cell carcinoma was 86.05％,83.72％,88.37％and 86.05％,respectively.Conclusion Radiomics models based on gray-scale ultrasound could be used to differentiate peripheral lung adenocarcinoma and lung squamous cell carcinoma.