Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer. Front Oncol 2023;13:1179025
Date
07/03/2023Pubmed ID
37397361Pubmed Central ID
PMC10313133DOI
10.3389/fonc.2023.1179025Scopus ID
2-s2.0-85164403482 (requires institutional sign-in at Scopus site) 4 CitationsAbstract
BACKGROUND: Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method.
METHODS: Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values.
RESULTS: The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue.
CONCLUSION: The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.
Author List
To T, Lu T, Jorns JM, Patton M, Schmidt TG, Yen T, Yu B, Ye DHAuthors
Taly Gilat-Schmidt PhD Associate Professor of Biomedical Engineering in the Biomedical Engineering department at Marquette UniversityJulie M. Jorns MD Professor in the Pathology department at Medical College of Wisconsin
Tina W F Yen MD, MS Professor in the Surgery department at Medical College of Wisconsin
Bing Yu PH.D. Assistant Professor of Biomedical Engineering in the Biomedical Engineering department at Marquette University
