期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 39, 期 2, 页码 534-542出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2019.2933656
关键词
Training; Deep learning; Breast cancer; Tumors; Immune system; Indexes; Convolutional neural networks; Deep learning; image segmentation; breast cancer; histopathology; digital pathology
类别
资金
- Academy of Finland [313921, 314558]
- Business Finland [269/31/2015]
- KAUTE Foundation
- Finnish Foundation for Technology Promotion
- Orion Research Foundation
- Academy of Finland (AKA) [314558, 313921, 314558, 313921] Funding Source: Academy of Finland (AKA)
Immunohistochemistry (IHC) of ER, PR, and Ki-67 are routinely used assays in breast cancer diagnostics. Determination of the proportion of stained cells (labeling index) should be restricted on malignant epithelial cells, carefully avoiding tumor infiltrating stroma and inflammatory cells. Here, we developed a deep learning based digital mask for automated epithelial cell detection using fluoro-chromogenic cytokeratin-Ki-67 double staining and sequential hematoxylin-IHC staining as training material. A partially pre-trained deep convolutional neural network was fine-tuned using image batches from 152 patient samples of invasive breast tumors. Validity of the trained digital epithelial cell masks was studied with 366 images captured from 98 unseen samples, by comparing the epithelial cell masks to cytokeratin images and by visual evaluation of the brightfield images performed by two pathologists. A good discrimination of epithelial cells was achieved (AUC of mean ROC = 0.93; defined as the area under mean receiver operating characteristics), and well in concordance with pathologists' visual assessment (4.01/5 and 4.67/5). The effect of epithelial cell masking on the Ki-67 labeling index was substantial. 52 tumor images initially classified as low proliferation (Ki-67 < 14%) without epithelial cell masking were re-classified as high proliferation (Ki-67 < 14%) after applying the deep learning based epithelial cell mask. The digital epithelial cell masks were found applicable also to IHC of ER and PR. We conclude that deep learning can be applied to detect carcinoma cells in breast cancer samples stained with conventional brightfield IHC.
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