Cytokeratin-Supervised Deep Learning for Automatic Recognition of Epithelial Cells in Breast Cancers Stained for ER, PR, and Ki-67.

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.

Epilepsy in neuropathologically verified Alzheimer's disease.

PURPOSE: Subjects with Alzheimer's disease (AD) have been shown to be at a higher risk for epilepsy. The vast majority of the previous studies have not included a full neuropathological examination. METHODS: The objective of this study was to assess the prevalence of epilepsy and clinicopathological characteristics in a well-defined study group of 64 subjects with AD. We evaluated the clinicopathological findings in 64 subjects (mean age at death 85 ±â€¯8.6 years) from a longitudi-nal study cohort of patients with dementia. RESULTS: Eleven out of the 64 subjects (17%) had a history of epilepsy, which is comparable to previous studies. The subjects with AD and epilepsy were significantly younger at the time of AD diagnosis and at the time of hospitalisation. In addition, their duration of AD was longer. Concomitant neuropathology in addition to AD was common in both groups and the ApoE genotypes did not differ significantly between the groups. CONCLUSION: The strength of this study is a thorough neuropathological examination of all study subjects. Our findings support the previous literature regarding the prevalence of epilepsy in subjects with AD. We have shown that the subjects with AD and epilepsy differ significantly from the subjects without epilepsy.