Objective:To explore the diagnostic value of combining Ki67, molecular subtyping, and ultrasonographic parameters in predicting axillary lymph node metastasis in breast cancer.Methods:200 breast cancer patients who were admitted to Meizhou People’s Hospital from Jan. 2020 to Dec. 2022 were collected. Based on the presence or absence of axillary lymph node metastasis in breast cancer, the patients were divided into an axillary lymph node metastasis group and a non-axillary lymph node metastasis group. Age, clinical stage, tumor location, tumor size, degree of differentiation, boundary, blood flow, echo, calcification, morphology, vascular invasion, Ki67, molecular typing, resistance index (RI) , shear wave velocity were collected. Multivariate Logistic regression analysis was used to screen the risk factors for axillary lymph node metastasis of breast cancer, and receiver operating characteristic curve (ROC) was used to evaluate the clinical value of ki67, molecular typing combined with ultrasound parameters in the diagnosis of axillary lymph node metastasis of breast cancer.Results:There were no statistically significant differences in age, clinical stage, tumor location, tumor size, differentiation degree, boundary, blood flow, echo or calcification between the axillary lymph node metastasis group and the non-axillary lymph node metastasis group ( t=0.80, χ20.13, χ2=0.14, χ2=0.90, χ2=0.64, χ2=1.03, χ2=0.04, χ2=0.34, χ2=1.2, P>0.05) , while there were statistically significant differences in morphology, vascular invasion, Ki67, molecular classification, RI and shear wave velocity between the two groups ( χ2=12.01, χ2=8.75, χ2=11.36, χ2=11.43, t=6.34, t=7.25, P<0.05) . Multivariate Logistic regression analysis showed that vascular invasion, Ki67 high expression, triple negative breast cancer, RI and shear wave velocity were all risk factors for axillary lymph node metastasis ( OR=5.572,4.026,3.632,107.639,1.936, P<0.05) . ROC curve analysis results showed that the AUC of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was 0.620, 0.594, 0.744 and 0.792, respectively, and the AUC of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was 0.846. The AUC of the combination of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was higher than that of Ki67, molecular typing, RI and shear wave velocity alone ( Z=5.55,7.10,3.44,2.45, P<0.05) . Conclusions:High Ki67 expression, triple-negative breast cancer, lymphovascular invasion,RI, and shear wave velocity are all risk factors for axillary lymph node metastasis in breast cancer. The combined use of Ki67, molecular subtype, RI, and shear wave velocity can improve the diagnostic accuracy for axillary lymph node metastasis in breast cancer.

Objective:To explore the diagnostic value of combining Ki67, molecular subtyping, and ultrasonographic parameters in predicting axillary lymph node metastasis in breast cancer.Methods:200 breast cancer patients who were admitted to Meizhou People’s Hospital from Jan. 2020 to Dec. 2022 were collected. Based on the presence or absence of axillary lymph node metastasis in breast cancer, the patients were divided into an axillary lymph node metastasis group and a non-axillary lymph node metastasis group. Age, clinical stage, tumor location, tumor size, degree of differentiation, boundary, blood flow, echo, calcification, morphology, vascular invasion, Ki67, molecular typing, resistance index (RI) , shear wave velocity were collected. Multivariate Logistic regression analysis was used to screen the risk factors for axillary lymph node metastasis of breast cancer, and receiver operating characteristic curve (ROC) was used to evaluate the clinical value of ki67, molecular typing combined with ultrasound parameters in the diagnosis of axillary lymph node metastasis of breast cancer.Results:There were no statistically significant differences in age, clinical stage, tumor location, tumor size, differentiation degree, boundary, blood flow, echo or calcification between the axillary lymph node metastasis group and the non-axillary lymph node metastasis group ( t=0.80, χ20.13, χ2=0.14, χ2=0.90, χ2=0.64, χ2=1.03, χ2=0.04, χ2=0.34, χ2=1.2, P>0.05) , while there were statistically significant differences in morphology, vascular invasion, Ki67, molecular classification, RI and shear wave velocity between the two groups ( χ2=12.01, χ2=8.75, χ2=11.36, χ2=11.43, t=6.34, t=7.25, P<0.05) . Multivariate Logistic regression analysis showed that vascular invasion, Ki67 high expression, triple negative breast cancer, RI and shear wave velocity were all risk factors for axillary lymph node metastasis ( OR=5.572,4.026,3.632,107.639,1.936, P<0.05) . ROC curve analysis results showed that the AUC of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was 0.620, 0.594, 0.744 and 0.792, respectively, and the AUC of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was 0.846. The AUC of the combination of Ki67, molecular typing, RI and shear wave velocity in the diagnosis of axillary lymph node metastasis of breast cancer was higher than that of Ki67, molecular typing, RI and shear wave velocity alone ( Z=5.55,7.10,3.44,2.45, P<0.05) . Conclusions:High Ki67 expression, triple-negative breast cancer, lymphovascular invasion,RI, and shear wave velocity are all risk factors for axillary lymph node metastasis in breast cancer. The combined use of Ki67, molecular subtype, RI, and shear wave velocity can improve the diagnostic accuracy for axillary lymph node metastasis in breast cancer.

Objective:To explore the diagnostic value of clinical, multi-parameter magnetic resonance imaging (MP-MRI) combined with transrectal ultrasound elasticity data for prostate cancer.Methods:A retrospective analysis was conducted on patient data from November 2021 to March 2023 when transrectal prostate two-dimensional ultrasound, real-time strain elastography of the prostate, MP-MRI examination of the prostate, and prostate biopsy were performed simultaneously at the Meizhou People′s Hospital. We collected patient age, height, weight, free serum prostate specific antigen (fPSA), total prostate specific antigen (tPSA), fPSA/tPSA, MRI prostate imaging report and data system (PI-RADS) scores, and ultrasound elasticity values. Four predictive models for prostate cancer diagnosis were constructed using multivariate logistic regression for comparison, and the optimal model was selected to construct a column chart. The diagnostic performance of different models was evaluated using receiver operating characteristic (ROC) curves, and the diagnostic performance of column charts was evaluated using calibration curves.Results:This study included a total of 117 patients with 117 prostate lesions, 47 benign prostate lesions, and 70 prostate cancer lesions. There were statistically significant differences in age, fPSA, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity values between benign and malignant lesions patients (all P<0.01). The area under the curve (AUC) of the clinical model (age+ tPSA+ fPSA+ fPSA/tPSA), MRI model (PI-RADS score), ultrasound elastic model, and clinical+ MRI+ ultrasound elastic combined model for diagnosing prostate cancer were 0.86, 0.86, 0.92, and 0.98, respectively. Conclusions:Compared with a single diagnostic model, the combination of age, tPSA, fPSA/tPSA, PI-RADS scores, and ultrasound elasticity value model can improve the diagnostic rate of prostate cancer.