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Comparison of Traditional Radiomics, Deep Learning Radiomics and Fusion Methods for Axillary Lymph Node Metastasis Prediction in Breast Cancer

无线电技术 乳腺癌 腋窝淋巴结 人工智能 医学 淋巴结 深度学习 磁共振成像 机器学习 淋巴结转移 放射科 计算机科学 转移 癌症 内科学
作者
Xue Li,Lifeng Yang,Xiong Jiao
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:30 (7): 1281-1287 被引量:31
标识
DOI:10.1016/j.acra.2022.10.015
摘要

Rationale and Objectives

Accurate identification of axillary lymph node (ALN) status in breast cancer patients is important for determining treatment options and avoiding axillary overtreatments. Our study aims to comprehensively compare the performance of the traditional radiomics model, deep learning radiomics model, and the fusion models in evaluating breast cancer ALN status based on dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) images.

Materials and Methods

The handcrafted radiomics features and deep features were extracted from 3062 DCE-MRI images. The feature selection was performed by applying mutual information and feature recursive elimination algorithms. The traditional radiomics model and deep learning radiomics model were built using the optimal features and machine learning classifiers, respectively. The fusion models for distinguishing axillary lymph node status were constructed using two fusion strategies. The performance of the models with MRI-reported lymphadenopathy or suspicious nodes to evaluate axillary lymph node status was also compared.

Results

The decision fusion model, with the integration of the radiomics features and deep learning features at the decision level, achieved an area under the curve (AUC) of 0.91 (95% confidence interval (CI): 0.879-0.937), which was higher than that of the traditional radiomics model and deep learning radiomics model. The results of the decision fusion model with clinical characteristic yielded an AUC of 0.93 (95% CI: 0.899-0.951), which was also superior to other models incorporating clinical characteristic.

Conclusion

This study demonstrates the effectiveness of the fusion models for predicting axillary lymph node metastasis in breast cancer.
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