Objective To investigate the utility of combining breast mass growth orientation quantification with the S-Detect technique for predicting axillary lymph node(ALN)metastasis in breast cancer.Methods Data was collected from 163 breast cancer patients admitted to our hospital between March 2023 and October 2024,who were categorized into metastatic(n=62)and non-metastatic(n=101)groups based on ALN pathology results.All patients underwent routine preoperative ultrasound and S-Detect examination.Univariate and multivariate regression analyses were performed to assess the correlation between each observational index and ALN metastasis.Significant indexes were identified through screening,leading to the establishment of a logistic regression prediction model.The predictive value of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Results The univariate analysis revealed statistically significant differences(P<0.05)in the maximum diameter of the mass,border characteristics,margin features,calcification patterns,orientation angle,and blood flow between the two groups.Multifactorial analysis demonstrated that calcification,border characteristics,orientation angle,margin features,and maximum diameter independently influenced the prediction of axillary lymph node(ALN)status in breast cancer patients(P<0.05).Consequently,a logistic regression prediction model was constructed as follows:Y=-7.995+2.299×maximal diameter+1.171×border+2.137×margin+1.397×calcication+0.034×orientation angle.The area under curve(AUC)for this combined prediction model was 0.869 which significantly outperformed each independent influencing factor alone(P<0.05),indicating good agreement between this joint prediction model and pathological results(Kappa=0.701,P<0.05).Conclusions Quantification of the orientation angle of a breast mass aids in predicting axillary lymph node(ALN)metastasis and enhances the interpretation and application of non-parallel orientations.The combination of quantifying growth orientation based on breast mass with artificial intelligence S-Detect technique demonstrates promising predictive value for ALN metastasis in breast cancer,providing a reference basis for personalized treatment.

Pelvic organ prolapse (POP), whose etiology is influenced by genetic and clinical risk factors, considerably impacts women's quality of life. However, the genetic underpinnings in non-European populations and comprehensive risk models integrating genetic and clinical factors remain underexplored. This study constructed the first polygenic risk score (PRS) for POP in the Chinese population by utilizing 20 disease-associated variants from the largest existing genome-wide association study. We analyzed a discovery cohort of 576 cases and 623 controls and a validation cohort of 264 cases and 200 controls. Results showed that the case group exhibited a significantly higher PRS than the control group. Moreover, the odds ratio of the top 10% risk group was 2.6 times higher than that of the bottom 10%. A high PRS was significantly correlated with POP occurrence in women older than 50 years old and in those with one or no childbirths. As far as we know, the integrated prediction model, which combined PRS and clinical risk factors, demonstrated better predictive accuracy than other existing PRS models. This combined risk assessment model serves as a robust tool for POP risk prediction and stratification, thereby offering insights into individualized preventive measures and treatment strategies in future clinical practice.
Humans ; Female ; Pelvic Organ Prolapse/epidemiology* ; Middle Aged ; Risk Assessment/methods* ; China/epidemiology* ; Multifactorial Inheritance ; Aged ; Risk Factors ; Genome-Wide Association Study ; Genetic Predisposition to Disease ; Case-Control Studies ; Adult ; Polymorphism, Single Nucleotide ; Genetic Risk Score ; East Asian People

Objective To evaluate the predictive value of dual-modality ultrasound,incorporating conventional ultrasound and ultrasound elastography,in combination with S-Detect for cervical lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC).Methods A retrospective analysis was conducted on the clinical data of 135 patients diagnosed with PTC who received treatment at the First Affiliated Hospital of Shihezi University between November 2023 and August 2024.For all patients,clinical baseline characteristics,conventional ultrasound findings,ultrasound elastography results,and S-Detect analysis data were collected.Independent predictors of CLNM in PTC were identified,and predictive models were developed.Receiver operating characteristic(ROC)curves were generated to compare the area under the curve(AUC)of the models.The most effective predictive model was selected to construct a risk probability nomogram,and the predictive performance and clinical applicability of this nomogram were subsequently evaluated.Results Age,maximum nodule diameter,boundary characteristics,capsular invasion,transverse-sectional morphological findings assessed by S-Detect,and ECI-based elasticity grading were identified as independent predictors of CLNM in PTC(all P<0.05).The AUC of the predictive model constructed using these six variables was 0.890(95%CI:0.835～0.945).The calibration curve demonstrated strong agreement between predicted and observed outcomes,and decision curve analysis indicated that the nomogram provided a favorable net clinical benefit within a threshold probability range of 2%to 91.5%.Conclusions Age,maximum nodule diameter,boundary characteristics,capsular invasion,sonographic features assessed by S-Detect in the transverse plane,and ECI-based elasticity grading are independent predictors of CLNM in PTC.A nomogram model incorporating these parameters demonstrates effective performance in predicting the likelihood of CLNM.

Objective To evaluate the predictive value of dual-modality ultrasound,incorporating conventional ultrasound and ultrasound elastography,in combination with S-Detect for cervical lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC).Methods A retrospective analysis was conducted on the clinical data of 135 patients diagnosed with PTC who received treatment at the First Affiliated Hospital of Shihezi University between November 2023 and August 2024.For all patients,clinical baseline characteristics,conventional ultrasound findings,ultrasound elastography results,and S-Detect analysis data were collected.Independent predictors of CLNM in PTC were identified,and predictive models were developed.Receiver operating characteristic(ROC)curves were generated to compare the area under the curve(AUC)of the models.The most effective predictive model was selected to construct a risk probability nomogram,and the predictive performance and clinical applicability of this nomogram were subsequently evaluated.Results Age,maximum nodule diameter,boundary characteristics,capsular invasion,transverse-sectional morphological findings assessed by S-Detect,and ECI-based elasticity grading were identified as independent predictors of CLNM in PTC(all P<0.05).The AUC of the predictive model constructed using these six variables was 0.890(95%CI:0.835～0.945).The calibration curve demonstrated strong agreement between predicted and observed outcomes,and decision curve analysis indicated that the nomogram provided a favorable net clinical benefit within a threshold probability range of 2%to 91.5%.Conclusions Age,maximum nodule diameter,boundary characteristics,capsular invasion,sonographic features assessed by S-Detect in the transverse plane,and ECI-based elasticity grading are independent predictors of CLNM in PTC.A nomogram model incorporating these parameters demonstrates effective performance in predicting the likelihood of CLNM.

Objective To investigate the utility of combining breast mass growth orientation quantification with the S-Detect technique for predicting axillary lymph node(ALN)metastasis in breast cancer.Methods Data was collected from 163 breast cancer patients admitted to our hospital between March 2023 and October 2024,who were categorized into metastatic(n=62)and non-metastatic(n=101)groups based on ALN pathology results.All patients underwent routine preoperative ultrasound and S-Detect examination.Univariate and multivariate regression analyses were performed to assess the correlation between each observational index and ALN metastasis.Significant indexes were identified through screening,leading to the establishment of a logistic regression prediction model.The predictive value of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Results The univariate analysis revealed statistically significant differences(P<0.05)in the maximum diameter of the mass,border characteristics,margin features,calcification patterns,orientation angle,and blood flow between the two groups.Multifactorial analysis demonstrated that calcification,border characteristics,orientation angle,margin features,and maximum diameter independently influenced the prediction of axillary lymph node(ALN)status in breast cancer patients(P<0.05).Consequently,a logistic regression prediction model was constructed as follows:Y=-7.995+2.299×maximal diameter+1.171×border+2.137×margin+1.397×calcication+0.034×orientation angle.The area under curve(AUC)for this combined prediction model was 0.869 which significantly outperformed each independent influencing factor alone(P<0.05),indicating good agreement between this joint prediction model and pathological results(Kappa=0.701,P<0.05).Conclusions Quantification of the orientation angle of a breast mass aids in predicting axillary lymph node(ALN)metastasis and enhances the interpretation and application of non-parallel orientations.The combination of quantifying growth orientation based on breast mass with artificial intelligence S-Detect technique demonstrates promising predictive value for ALN metastasis in breast cancer,providing a reference basis for personalized treatment.