Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks

Yan-Wei Lee; Chiun-Sheng Huang; Chung-Chih Shih; Ruey-Feng Chang

doi:10.1016/j.compbiomed.2020.104206

Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks

Comput Biol Med. 2021 Mar:130:104206. doi: 10.1016/j.compbiomed.2020.104206. Epub 2020 Dec 31.

Authors

Yan-Wei Lee¹, Chiun-Sheng Huang², Chung-Chih Shih³, Ruey-Feng Chang⁴

Affiliations

¹ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan.
² Department of Surgery, College of Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, 100, Taiwan.
³ Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan.
⁴ Department of Computer Science and Information Engineering, National Taiwan University, Taipei, 10617, Taiwan; Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 10617, Taiwan; Graduate Institute of Network and Multimedia, National Taiwan University, Taipei, 10617, Taiwan; MOST Joint Research Center for AI Technology and All Vista Healthcare, Taipei, 10617, Taiwan. Electronic address: rfchang@csie.ntu.edu.tw.

PMID: 33421823
DOI: 10.1016/j.compbiomed.2020.104206

Abstract

Deep learning (DL) algorithms have been proven to be very effective in a wide range of computer vision applications, such as segmentation, classification, and detection. DL models can automatically assess complex medical image scenes without human intervention and can be applied as a second reader to provide an additional opinion for the physician. To predict the axillary lymph node (ALN) metastatic status in patients with early-stage breast cancer, a deep learning-based computer-aided prediction system for ultrasound (US) images was proposed. A total of 153 women with breast tumor US images were involved in this study; there were 59 patients with metastasis and 94 patients without ALN metastasis. A deep learning-based computer-aided prediction (CAP) system using the tumor region and peritumoral tissue in ultrasound (US) images were employed to determine the ALN status in breast cancer. First, we adopted Mask R-CNN as our tumor detection and segmentation model to obtain the tumor localization and region. Second, the peritumoral tissue was extracted from the US image, which reflects metastatic progression. Third, we used the DL model to predict ALN metastasis. Finally, the simple linear iterative clustering (SLIC) superpixel segmentation method and the LIME explanation algorithm were employed to explain how the model makes decisions. The experimental results indicated that the DL model had the best prediction performance on tumor regions with 3 mm thick peritumoral tissue, and the accuracy, sensitivity, specificity, and AUC were 81.05% (124/153), 81.36% (48/59), 80.85% (76/94), and 0.8054, respectively. The results indicated that the proposed CAP system could help determine the ALN status in patients with early-stage breast cancer. The results reveal that the proposed CAP model, which combines primary tumor and peritumoral tissue, is an effective method to predict the ALN status in patients with early-stage breast cancer.

Keywords: Axillary lymph node status; Breast cancer; Breast ultrasound; Computer-aided prediction; Convolutional neural network; Deep learning.

MeSH terms

Breast Neoplasms* / diagnostic imaging
Female
Humans
Lymph Nodes / diagnostic imaging
Lymphatic Metastasis / diagnostic imaging
Neural Networks, Computer
Retrospective Studies