UV-induced local immunosuppression in the tumour microenvironment of eccrine porocarcinoma and poroma.

Eccrine porocarcinoma (EPC) is a rare malignant adnexal tumour of the skin. Part of EPCs develop from their benign counterpart, poroma (EP), with chronic light exposure and immunosuppression hypothesized to play a role in the malignant transformation. However, the impact of chronic light exposure on the microenvironment of EPCs and EPs has not been investigated yet. Although the clinical relevance of tumour infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLSs) has been established in various tumours, their distribution and significance in EPCs and EPs is still poorly understood. We characterized the distribution of TILs and TLSs using CD3, CD4, CD8, CD20 immunohistochemistry in a cohort of 10 EPCs and 49 EPs. We then classified our samples using solar-elastosis grading, analyzing the influence of ultraviolet (UV) damage on TIL density. A negative correlation between UV damage and TIL density was observed (CD4 r = -0.286, p = 0.04. CD8 r = -0.305, p = 0.033). No significant difference in TIL density was found between EPCs and EPs. TLS was scarse with the presence rate 10% in EPCs and 8.3% in EPs. The results suggest that UV has an immunosuppressive effect on the microenvironment of EPCs and EPs.

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.

Optimized JPEG 2000 Compression for Efficient Storage of Histopathological Whole-Slide Images.

BACKGROUND: Whole slide images (WSIs, digitized histopathology glass slides) are large data files whose long-term storage remains a significant cost for pathology departments. Currently used WSI formats are based on lossy image compression alogrithms, either using JPEG or its more efficient successor JPEG 2000. While the advantages of the JPEG 2000 algorithm (JP2) are commonly recognized, its compression parameters have not been fully optimized for pathology WSIs. METHODS: We defined an optimized parametrization for JPEG 2000 image compression, designated JP2-WSI, to be used specifically with histopathological WSIs. Our parametrization is based on allowing a very high degree of compression on the background part of the WSI while using a conventional amount of compression on the tissue-containing part of the image, resulting in high overall compression ratios. RESULTS: When comparing the compression power of JP2-WSI to the commonly used fixed 35:1 compression ratio JPEG 2000 and the default image formats of proprietary Aperio, Hamamatsu, and 3DHISTECH scanners, JP2-WSI produced the smallest file sizes and highest overall compression ratios for all 17 slides tested. The image quality, as judged by visual inspection and peak signal-to-noise ratio (PSNR) measurements, was equal to or better than the compared image formats. The average file size by JP2-WSI amounted to 15, 9, and 16 percent, respectively, of the file sizes of the three commercial scanner vendors' proprietary file formats (3DHISTECH MRXS, Aperio SVS, and Hamamatsu NDPI). In comparison to the commonly used 35:1 compressed JPEG 2000, JP2-WSI was three times more efficient. CONCLUSIONS: JP2-WSI allows very efficient and cost-effective data compression for whole slide images without loss of image information required for histopathological diagnosis.

Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry.

Evaluation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is subject to interobserver variation and lack of reproducibility. Digital image analysis (DIA) has been shown to improve the consistency and accuracy of the evaluation and its use is encouraged in current testing guidelines. We studied whether digital image analysis using a free software application (ImmunoMembrane) can assist in interpreting HER2 IHC in equivocal 2+ cases. We also compared digital photomicrographs with whole-slide images (WSI) as material for ImmunoMembrane DIA. We stained 750 surgical resection specimens of invasive breast cancers immunohistochemically for HER2 and analysed staining with ImmunoMembrane. The ImmunoMembrane DIA scores were compared with the originally responsible pathologists' visual scores, a researcher's visual scores and in situ hybridisation (ISH) results. The originally responsible pathologists reported 9.1 % positive 3+ IHC scores, for the researcher this was 8.4 % and for ImmunoMembrane 9.5 %. Equivocal 2+ scores were 34 % for the pathologists, 43.7 % for the researcher and 10.1 % for ImmunoMembrane. Negative 0/1+ scores were 57.6 % for the pathologists, 46.8 % for the researcher and 80.8 % for ImmunoMembrane. There were six false positive cases, which were classified as 3+ by ImmunoMembrane and negative by ISH. Six cases were false negative defined as 0/1+ by IHC and positive by ISH. ImmunoMembrane DIA using digital photomicrographs and WSI showed almost perfect agreement. In conclusion, digital image analysis by ImmunoMembrane can help to resolve a majority of equivocal 2+ cases in HER2 IHC, which reduces the need for ISH testing.