ObjectiveTo explore the value of ultrasound real-time tissue elastography in the differential diagnosis between benign and malignant breast lesions.Methods A total of 131 cases of patients with breast lesions who underwent ultrasound examination in the People’s Hospital of Guangan City between December 2010 and December 2015 were enrolled as the research object. The patients took conventional color Doppler ultrasound diagnosis firstly, and then took ultrasound real-time tissue elastography diagnosis. The lesions were scored with improved 5-scoring system respectively. By the strain ratio measure method equipped with the ultrasonic machine, strain ratio of the lesion was calculated, with 3.08 as the cut-off pont. The results were campared with the pathologic diagnosis.ResultsThere were 182 breast lumps in the 131 patients. The conventional ultrasound examination detected 128 benign lesions and 54 malignant lesions. By ultrasound real-time tissue elastography examination, there were 121 benign tumors and 61 malignant tumors. For the benign tumors, the elasticity imaging score was 1.74±0.81, and the elastic strain rate ratio was 1.83±1.22; for the malignant tumors, the elasticity imaging score was 4.45±0.59, and the elastic strain rate ratio was 8.68±5.58. The 182 breast lumps were all removed by surgical resection, and the pathologic examination showed there were 121 benign lesions and 61 malignant lesions. The accuracy, sensitivity and specificity of conventional ultrasonic diagnosis of breast malignant lesions was 76.4%, 59.0% and 85.1%, respectively; while the indexes of ultrasound real-time tissue elastography diagnosis of breast malignant lesions was 96.7%, 95.1% and 97.5%, respectively, and the differences were statistically significant (P<0.05).ConclusionReal-time tissue elastography is helpful in the differential diagnosis between malignant and benign breast lesions.
ObjectivesTo explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.MethodsA case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.ResultsThe decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.ConclusionsThe methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.