Abstract:

Objective

To assess the value of a deep learning-based nomogram in predicting axillary lymph node （ALN） status following neoadjuvant chemotherapy （NAC） in breast cancer patients.

Methods

Four hundred and fourteen ALN-positive breast cancer patients who received NAC between March 2020 and June 2023 were enrolled in this retrospective study and divided into a training set and an external test set.The training set consisted of 257 patients from the First Affiliated Hospital of Nanjing Medical University， while the external test set included 157 patients from the General Hospital of Eastern Theater Command.All patients were divided into pathologically complete response （pCR） and non-pCR（npCR） groups based on the pathology results of ALN surgery post-NAC.A deep learning model based on the ResNet50 architecture was trained and established using pre-NAC ultrasound images of breast tumors.Univariate and multivariate logistic regression analyses were performed on the training set to identify independent risk factors for post-NAC ALN status.These independent risk factors were then used to construct a clinical model.A deep learning-based nomogram was constructed by combining independent risk factors and deep learning predictive probabilities.The performance of the models was evaluated using receiver operating characteristic （ROC） curve， calibration curve， decision curve analysis， and clinical impact curve.Two radiologists with different experience levels independently predicted ALN status in the external test set based on ultrasound images and pre-NAC immunohistochemical results， and performed a second prediction with the assistance of the deep learning-based nomogram.The two prediction results were compared.

Results

Estrogen receptor （ER） and human epidermal growth factor receptor 2 （Her-2） were identifled as independent risk factors for predicting post-NAC ALN status.The area under the curve （AUC） values of the clinical model， deep learning model， and deep learning-based nomogram were 0.724， 0.872， and 0.878 in the training set， and 0.698， 0.831， and 0.859 in the external test set， respectively.The deep learning-based nomogram outperformed the clinical model （both P＜0.001 in training and external test sets） and showed superior performance to the deep learning model in the external test set （P=0.024）.The AUC values of radiologist 1 （low-experience） and radiologist 2 （high-experience） for independent judgment were 0.570 and 0.606， respectively， both signiflcantly lower than those of the deep learning model and the deep learningbased nomogram （all P＜0.001）.With the assistance of the deep learning-based nomogram， the AUC values of radiologist 1 and radiologist 2 improved to 0.796 and 0.807， respectively， showing statistically signiflcant differences compared to independent judgment （both P＜0.001）.

Conclusion

The deep learning-based nomogram based on pre-NAC ultrasound images can effectively predict the pathological status of ALN in breast cancer patients after NAC treatment， providing more evidence for the development of personalized treatment plans.

Key words: Ultrasonography, Breast cancer, Axillary lymph nodes, Neoadjuvant chemotherapy, Deep learning