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10.1186/s13000-017-0658-8 http://scihub22266oqcxt.onion/10.1186/s13000-017-0658-8 C5604347!5604347 !28923066     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28923066 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Diagn+Pathol 2017 ; 12 (1 ): 69 Nephropedia Template TP {{tp|p=28923066 |t=2017. Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate |pdf=|usr=}}{{28923066 }} gab.com Text Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate JO: Diagn Pathol http://www.kidney.de/pget.php?&tw=1&pmid=28923066 #FOAvip BACKGROUND: Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC). METHOD: We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture. RESULTS: The overlap between manual and transferred tumor masks ranged from 20%-90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10(-6)). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10(-10)). CONCLUSIONS: In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies. Twit Text FOAVip Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate ????Diagn Pathol http://www.kidney.de/pget.php?&tw=1&pmid=28923066 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28923066 #FOAvip English Wikipedia <ref>{{Cite pmid|28923066 }}</ref> Data integration from pathology slides for quantitative imaging of multiple cell types within the tumor immune cell infiltrate #MMPMID28923066 Ma Z ; Shiao SL ; Yoshida EJ ; Swartwood S ; Huang F ; Doche ME ; Chung AP ; Knudsen BS ; Gertych A Diagn Pathol 2017[Sep]; 12 (1 ): 69 PMID28923066 show ga BACKGROUND: Immune cell infiltrates (ICI) of tumors are scored by pathologists around tumor glands. To obtain a better understanding of the immune infiltrate, individual immune cell types, their activation states and location relative to tumor cells need to be determined. This process requires precise identification of the tumor area and enumeration of immune cell subtypes separately in the stroma and inside tumor nests. Such measurements can be accomplished by a multiplex format using immunohistochemistry (IHC). METHOD: We developed a pipeline that combines immunohistochemistry (IHC) and digital image analysis. One slide was stained with pan-cytokeratin and CD45 and the other slide with CD8, CD4 and CD68. The tumor mask generated through pan-cytokeratin staining was transferred from one slide to the other using affine image co-registration. Bland-Altman plots and Pearson correlation were used to investigate differences between densities and counts of immune cell underneath the transferred versus manually annotated tumor masks. One-way ANOVA was used to compare the mask transfer error for tissues with solid and glandular tumor architecture. RESULTS: The overlap between manual and transferred tumor masks ranged from 20%-90% across all cases. The error of transferring the mask was 2- to 4-fold greater in tumor regions with glandular compared to solid growth pattern (p < 10(-6)). Analyzing data from a single slide, the Pearson correlation coefficients of cell type densities outside and inside tumor regions were highest for CD4 + T-cells (r = 0.8), CD8 + T-cells (r = 0.68) or CD68+ macrophages (r = 0.79). The correlation coefficient for CD45+ T- and B-cells was only 0.45. The transfer of the mask generated an error in the measurement of intra- and extra- tumoral CD68+, CD8+ or CD4+ counts (p < 10(-10)). CONCLUSIONS: In summary, we developed a general method to integrate data from IHC stained slides into a single dataset. Because of the transfer error between slides, we recommend applying the antibody for demarcation of the tumor on the same slide as the ICI antibodies. |Biomarkers, Tumor/*metabolism [MESH] |Breast Neoplasms/*pathology [MESH] |Cell Count [MESH] |Cohort Studies [MESH] |Female [MESH] |Humans [MESH] |Image Processing, Computer-Assisted/*methods [MESH] |Immunohistochemistry [MESH] |Inflammation/pathology [MESH] |Keratins/metabolism [MESH]Data integration from pathology slides for quantitative imaging of mul(epmc).pdf DeepDyve Pubget Overpricing