Cytomine: toward an open and collaborative software platform for digital pathology bridged to molecular investigations

Purpose Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are s...

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Bibliographic Details
Main Authors: Rubens, Ulysse (Author) , Longuespée, Rémi (Author)
Format: Article (Journal)
Language:English
Published: 2019
In: Proteomics. Clinical applications
Year: 2018, Volume: 13, Issue: 1
ISSN:1862-8354
DOI:10.1002/prca.201800057
Online Access:Verlag, Volltext: https://doi.org/10.1002/prca.201800057
Verlag: https://onlinelibrary.wiley.com/doi/abs/10.1002/prca.201800057
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Author Notes:Ulysse Rubens, Renaud Hoyoux, Laurent Vanosmael, Mehdy Ouras, Maxime Tasset, Christopher Hamilton, Rémi Longuespée, and Raphaël Marée

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520 |a Purpose Digital histology is being increasingly used in research and clinical applications. In parallel, new tissue imaging methods (e.g., imaging mass spectrometry) are currently regarded as very promising approaches for better molecular diagnosis in pathology. However, these new data sources are still often underexploited because of the lack of collaborative software to share and correlate information for multimodal analysis. Experimental design The open science paradigm is followed to develop new features in the web-based Cytomine software to support next-generation digital pathology bridged to molecular investigations. Results New open-source developments allow to explore whole-slide classical histology with Matrix Assisted Laser Desorption Ionisation (MALDI) imaging and to support preprocessing for biomarker discovery using laser microdissection-based microproteomics. Conclusions and clinical relevance The updated version of Cytomine is the first open and web-based tool to enable sharing data from classical histology, molecular imaging, and cell counting for proteomics preprocessing. It holds good promise to fulfill imminent needs in molecular histopathology. 
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