Abstract：
:Remodeling of the collagen architecture in the extracellular matrix (ECM) has been implicated in ovarian cancer. To quantify these alterations we implemented a form of 3D texture analysis to delineate the fibrillar morphology observed in 3D Second Harmonic Generation (SHG) microscopy image data of normal (1) and high risk (2) ovarian stroma, benign ovarian tumors (3), low grade (4) and high grade (5) serous tumors, and endometrioid tumors (6). We developed a tailored set of 3D filters which extract textural features in the 3D image sets to build (or learn) statistical models of each tissue class. By applying k-nearest neighbor classification using these learned models, we achieved 83-91% accuracies for the six classes. The 3D method outperformed the analogous 2D classification on the same tissues, where we suggest this is due the increased information content. This classification based on ECM structural changes will complement conventional classification based on genetic profiles and can serve as an additional biomarker. Moreover, the texture analysis algorithm is quite general, as it does not rely on single morphological metrics such as fiber alignment, length, and width but their combined convolution with a customizable basis set.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Wen B,Campbell KR,Tilbury K,Nadiarnykh O,Brewer MA,Patankar M,Singh V,Eliceiri KW,Campagnola PJdoi
10.1038/srep35734subject
Has Abstractpub_date
2016-10-21 00:00:00pages
35734issn
2045-2322pii
srep35734journal_volume
6pub_type
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pub_type： 已发布勘误
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journal_title：Scientific reports
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