Prognosticating axillary lymph node metastasis in breast cancer through integrated photoacoustic imaging, ultrasound, and clinical parameters.

PURPOSE: To develop and validate a predictive model for axillary lymph node metastasis (ALNM) in breast cancer (BC) by integrating clinicopathological factors, ultrasound features, and photoacoustic imaging-derived SO2 measurements, aiming to improve diagnostic accuracy and provide comprehensive clinical insights.

METHODS

A total of 317 BC patients were included, with the cohort split into a training set (70%) and a testing set (30%). Univariate and multivariate logistic regression identified key predictive factors, leading to the creation of three models: ModA (clinicopathological factors only), ModB (clinicopathological and ultrasound features), and ModC (clinicopathological, ultrasound, and SO2 measurements from photoacoustic imaging). De-Long test and ROC curve were used to evaluate and compare the diagnostic performance of the models.

RESULTS

Multivariate analysis showed that maximum diameter, Ki67 expression, AUS report and SO2 levels were identified as significant risk factors for ALNM. ModA achieved an AUC of 0.776 (95% CI: 0.691-0.862), ModB improved to 0.824 (95% CI: 0.738-0.909), and ModC demonstrated the highest performance with an AUC of 0.882 (95% CI: 0.815-0.950) in the testing set. The results highlight that the comprehensive model (ModC), integrating clinical, ultrasound, and photoacoustic imaging data, provides superior predictive accuracy for ALNM.

CONCLUSION

Integrating SO2 measurements with traditional clinical and ultrasound data can substantially enhance the prediction of ALNM in BC patients. This combined model offers a comprehensive and reliable decision support tool for the preoperative risk assessment of axillary lymph nodes in BC.