Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study

Background

Accurately assessing the status of axillary lymph nodes (ALNs) is essential for devising optimal surgical plans and making informed treatment decisions in breast cancer (BC) patients.

Aims

This study aims to develop an innovative nomogram based on pathomics to preoperatively predict ALN metastasis (ALNM) in BC.

Methods and Results

Our study performed a retrospective analysis on digital hematoxylin and eosin (H&E)-stained images obtained from 407 patients across two institutions who were allocated into a training cohort (TC; n = 203), an internal validation cohort (IVC; n = 136), and an external validation cohort (EVC; n = 68). Initially, the Mann–Whitney U-test and Spearman's rank correlation coefficient were utilized for feature selection, employing the least absolute shrinkage and selection operator (LASSO) regression for further refinement. For the evaluation of the predictive value of ALNM and other clinicopathological factors, we deployed both univariate (ULR) and multivariate (MLR) logistic regression analyses. Among the six machine learning (ML) algorithms, logistic regression, which demonstrated the highest area under the curve (AUC) value, was employed to establish the final nomogram model. The nomogram reliability and stability were assessed by analyzing the AUC of the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration plots. MLR analysis demonstrated estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), tumor size, and pathomics features as independent ALNM predictors. The nomogram demonstrated that the AUC in the IVC (0.783) surpassed that of the Path-score model (0.698) (DeLong test, p = 0.008558). Similarly, in the EVC, the nomogram surpassed the clinical model regarding AUC (0.738 vs. 0.574; DeLong test, p = 0.00494). Additionally, DCA analysis indicated a net clinical benefit associated with the nomogram.

Conclusion

Our study demonstrates the effectiveness of pathomics features in predicting ALNM in BC patients. Furthermore, the pathomics-based nomogram offers a valuable tool for personalized treatment planning in this patient population.