Predictive value of growth orientation quantification combined with S-Detect technique for axillary lymph node metastasis in breast cancer
10.3969/j.issn.1006-5725.2025.01.017
- VernacularTitle:生长方位量化联合S-Detect技术对乳腺癌腋窝淋巴结转移的预测价值
- Author:
Yaqian DENG
1
;
Wenxiao LI
;
Zelin XU
;
Jinmei MA
;
Tingting DU
;
Wen LIU
;
Jun LI
Author Information
1. 石河子大学第一附属医院超声科(新疆石河子 832008)
- Publication Type:Journal Article
- Keywords:
ultrasound;
S-Detect technique;
breast cancer;
ALN;
growth orientation;
quantifi-cation
- From:
The Journal of Practical Medicine
2025;41(1):100-107
- CountryChina
- Language:Chinese
-
Abstract:
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.
