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• W2562238556 abstract "Abstract BACKGROUND: Since the early 20th century, the pathological classification of breast cancer has been based primarily on the visual analysis of H&E stained images using conventional 2D microscopy. With the recent development of new state-of-the-art microscopy platforms, such as fluorescent Lightsheet microscopy (LSM), the rapid acquisition of three dimensional (3D) images directly from tissue samples up to several millimeters in thickness is now possible. The aim of this project is to develop methods to perform LSM on formalin fixed paraffin embedded (FFPE) breast tissue samples and to use this approach to identify 3D morphological hallmarks of breast carcinogenesis, which may aid in breast cancer research and diagnostics. METHODS: 30 breast tissue samples, including normal breast, ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC), were collected. To prepare the tissue for LSM, we obtained 1 mm diameter tissue cores from the FFPE blocks, which we deparaffinized, permeabilized with Triton X-100, treated with sodium borohydride for autofluorescence reduction, stained with Gel Green for nucleus detection and clarified using a modified Scale A2 solution to increase light penetration. We then designed and implemented an image analysis pipeline to obtain measurements from the 3D images and to build classification models. The pipeline for nuclear segmentation consisted of adaptive thresholding, morphological operations and watershed segmentation, followed by the extraction of morphometric features (11 morphology, 7 intensity, 18 texture, and 5 spatial graph-based features). Lastly, we performed logistic regression with Lasso regularization to build LSM image feature-based models to classify cases into diagnostic categories. Model performance was assessed by computing the area under the curve (AUC) in cross-validation. RESULTS AND CONCLUSIONS: The deparaffinization, permabilization, clarification, and fluorescent staining protocol we developed enabled visualization of 3D breast architecture with sub-cellular resolution from FFPE specimens. To assess the diagnostic utility of LSM in breast pathology, we used the LSM-derived features to build classification models, which showed strong performance for the discrimination of normal breast from both DCIS and IBC (AUC = 0.94 in cross validation for both tasks). Morphological and spatial graph-based features were the strongest predictors of pathological diagnoses in the classification models. These data suggest that 3D morphometric and spatial features are highly informative of pathological diagnosis and may supplement conventional morphological and molecular approaches in breast cancer diagnostics. These results lay the ground work for future larger scale studies to more fully evaluate the utility of LSM for breast cancer research and diagnostics. Citation Format: Octavian Bucur, Humayun Irshad, Laleh Montaser-Kouhsari, Nicholas W. Knoblauch, Eun-Yeong Oh, Jonathan Nowak, Andrew H. Beck. 3D morphological hallmarks of breast carcinogenesis: Diagnosis of non-invasive and invasive breast cancer with Lightsheet microscopy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3477. doi:10.1158/1538-7445.AM2015-3477" @default.
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• W2562238556 date "2015-08-01" @default.
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• W2562238556 title "Abstract 3477: 3D morphological hallmarks of breast carcinogenesis: Diagnosis of non-invasive and invasive breast cancer with Lightsheet microscopy" @default.
• W2562238556 doi "https://doi.org/10.1158/1538-7445.am2015-3477" @default.
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