Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning

Wei-Bin Li; Zhi-Cheng Du; Yue-Jie Liu; Jun-Xue Gao; Jia-Gang Wang; Qian Dai; Wen-He Huang

doi:10.3389/fonc.2023.1219838

Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning

Front Oncol. 2023 Sep 1:13:1219838. doi: 10.3389/fonc.2023.1219838. eCollection 2023.

Authors

Wei-Bin Li^{1

2

3

4

5}, Zhi-Cheng Du^{1

2

3

4

6}, Yue-Jie Liu^{2

3

7}, Jun-Xue Gao^{2

3

7}, Jia-Gang Wang⁸, Qian Dai⁹, Wen-He Huang^{1

2

3

4}

Affiliations

¹ Cancer Center and Department of Breast and Thyroid Surgery, Xiang'an Hospital, School of Medicine, Xiamen University, Xiamen, China.
² Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China.
³ Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China.
⁴ Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
⁵ Department of Ultrasonic Medicine Affiliated Hospital of Xizang Minzu University, Xianyang, China.
⁶ National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China.
⁷ Department of Ultrasonic Medicine, Xiang'an Hospital, School of Medicine, Xiamen University, Xiamen, China.
⁸ Department of Ultrasonic Medicine of Shantou Central Hospital, Shantou, China.
⁹ School of Informatics, Xiamen University, Xiamen, China.

Abstract

Objective: To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients.

Methods: A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models.

Results: Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review.

Conclusion: A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.

Keywords: artificial intelligence; axillary lymph node metastasis; breast lesion; deep learning model; ultrasound video image.

Grants and funding

This work was supported by the Natural Science Foundation Committee of China (Grant No.32171363), Fujian Major Scientific and Technological Special Project for Social Development (Grant No.2020YZ016002), the Science and Technology Project of Xiamen Municipal Bureau of Science and Technology (Grant No. 3502Z20199047) and an open project of Xiamen’s Key Laboratory of Precision Medicine for Endocrine-Related Cancers (Grant No. XKLEC 2020KF02), The Fujian Provincial Medical Innovation Project (2020CXB053), Natural Science Foundation of Fujian Province,(No. 2021D035), Natural Science Foundation of Tibet, China (XZ202101ZR0087G).