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Radiomics model based on features of axillary lymphatic nodes to predict axillary lymphatic node metastasis in breast
Yong Tang1, Xiaoling Che2, Weijia Wang3
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Background:
Breast cancer (BC) is among the most common cancers worldwide. Machine learning-based radiomics model could predict axillary lymph node metastasis (ALNM) of BC accurately.
Purpose:
The purpose is to develop a machine learning model to predict ALNM of BC by focusing on the radiomics features of axillary lymphatic node (ALN).
Methods:
A group of 398 BC patients with 800 ALNs were retrospectively collected. A set of patient characteristics were obtained to form clinical factors. Three hundred and twenty-six radiomics features were extracted from each region of interest for ALN in contrast-enhanced computed tomography (CECT) image. A framework composed of four feature selection methods and 14 machine learning classification algorithms was systematically applied. A clinical model, a radiomics model, and a combined model were developed using a cross-validation approach and compared. Metrics of the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the performance of these models in the prediction of ALNM in BC.
Results:
Among the 800 cases of ALNs, there were 388 cases of positive metastasis (48.50%) and 412 cases of negative metastasis (51.50%). The baseline clinical model achieved the performance with an AUC = 0.8998 (95% CI [0.8540, 0.9457]). The radiomics model achieved an AUC = 0.9081 (95% CI [0.8640, 0.9523]). The combined model using the clinical factors and radiomics features achieved the best results with an AUC = 0.9305 (95% CI [0.8928, 0.9682]).
Conclusions:
Combinations of feature selection methods and machine learning-based classification algorithms can develop promising predictive models to predict ALNM in BC using CECT features. The combined model of clinical factors and radiomics features outperforms both the clinical model and the radiomic model.
