ArticleApplied immunohistochemistry & molecular morphology : AIMM2016
Image-guided Coring for Large-scale Studies in Molecular Pathology.
Article in Applied immunohistochemistry & molecular morphology : AIMM, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- RNA profiling and immunohistochemistry analyses of circRNAs in imatinib-resistant gastrointestinal stromal tumors.Journal of translational medicine · 2025Article
- Construction and analysis of tissue microarrays in the era of digital pathology: a pilot study targeting CDX1 and CDX2 in a colon cancer cohort of 612 patients.The journal of pathology. Clinical research · 2017Article
- Preparation of Formalin-fixed Paraffin-embedded Tissue Cores for both RNA and DNA Extraction.Journal of visualized experiments : JoVE · 2016Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Sampling of formalin-fixed paraffin-embedded (FFPE) tissue blocks is a critical initial step in molecular pathology. Image-guided coring (IGC) is a new method for using digital pathology images to guide tissue block coring for molecular analyses. The goal of our study is to evaluate the use of IGC for both tissue-based and nucleic acid-based projects in molecular pathology. First, we used IGC to construct a tissue microarray (TMA); second, we used IGC for FFPE block sampling followed by RNA extraction; and third, we assessed the correlation between nuclear counts quantitated from the IGC images and RNA yields. We used IGC to construct a TMA containing 198 normal and breast cancer cores. Histopathologic analysis showed high accuracy for obtaining tumor and normal breast tissue. Next, we used IGC to obtain normal and tumor breast samples before RNA extraction. We selected a random subset of tumor and normal samples to perform computational image analysis to quantify nuclear density, and we built regression models to estimate RNA yields from nuclear count, age of the block, and core diameter. Number of nuclei and core diameter were the strongest predictors of RNA yields in both normal and tumor tissue. IGC is an effective method for sampling FFPE tissue blocks for TMA construction and nucleic acid extraction. We identify significant associations between quantitative nuclear counts obtained from IGC images and RNA yields, suggesting that the integration of computational image analysis with IGC may be an effective approach for tumor sampling in large-scale molecular studies.
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Registered trials
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