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Abstract 46: 3D reconstruction and quantitative analysis of histology for prostate cancer

The integration of serial sectioning of tissue, digital whole slide imaging (WSI) and computational reconstruction algorithms enable the examination of histological samples in 3D at subcellular resolution. This allows visualizing and analyzing the imaged sample in its true three-dimensional context, offering a more comprehensive view on its spatial and morphological characteristics than that obtained via typical 2D examination. The advantages of reconstructing 3D models computationally using WSI over direct 3D imaging include the combination of high resolution, large sample sizes and compatibility with existing biochemical techniques such as in situ hybridization, immunohistochemistry and established histological staining and interpretation protocols. For this purpose, we developed a software pipeline to perform routine 3D reconstruction tasks for large image sizes and datasets in a fully automatic manner. The steps in our 3D reconstruction process are image acquisition, alignment of images to a shared coordinate space, and visualization of the reconstructed 3D data. We optimized algorithm selection and computationally intensive hyperparameter tuning using a quantitative benchmarking framework and Bayesian optimization. Further, we applied our existing pipeline for feature based analysis of tissue and extended it into 3D, allowing quantification and visualization of hundreds of features characterizing the tissue. We currently apply this system to characterize prostate cancer tumors. Prostate cancer is a heterogenous, often multifocal disease. In order to understand why and how certain tumor foci develop to a life-threatening disease over others, the tissue growth patterns and evolution of tumors with different genetic backgrounds need to be studied. We use mouse models of prostate cancer to study early tumor development connected to common genetic cancer alterations. By computing hundreds of features from the histology, and studying these in the spatial context, we gain important information of tumor characteristics as well as intra- and intertumor heterogeneity. In the future, we will further scale up the protocol to perform 3D reconstruction for serially sectioned human prostates in the near future. Citation Format: Pekka Ruusuvuori, Kimmo Kartasalo, Mira Valkonen, Masi Valkonen, Tapio Visakorpi, Matti Nykter, Leena Latonen. 3D reconstruction and quantitative analysis of histology for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 46.