Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study

Abstract

Background: Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy.

Methods: This multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated.

Results: The clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75 to 0.85 for the senior radiologist.

Conclusions: The clinical-radiomics model effectively predicts ALNM in breast cancer patients using noninvasive ultrasound features. Additionally, it enhances radiologists' diagnostic accuracy, potentially optimizing resource allocation in breast cancer management.

Keywords: axillary lymph node metastasis; breast cancer; predictive model; radiomics; ultrasound.

Figure 1.

Development and performance of the clinic-radiomics model. (A) Nomogram for predicting the probability of ALNM. (B-D) The risk-classification performance of the clinic-radiomics model in the internal (B), external (C) and prospective test set (D), respectively. Abbreviations: ALNM, axillary lymph node metastasis; LN, lymph node; US, ultrasound.

Figure 2.

The receiver operating characteristic curves for the prediction of the ALN status in the internal (A), external (B) and prospective test set (C).

Figure 3.

The ROC plots of the clinic-radiomics model and radiologists without and with artificial intelligence (AI) assistance in the prospective test set (A). Accuracy with or without AI-assisted diagnosis in the prospective test set (B).