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These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2342-13-9","type":"journal-article","created":{"date-parts":[[2013,3,13]],"date-time":"2013-03-13T23:16:12Z","timestamp":1363216572000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Histological image classification using biologically interpretable shape-based features"],"prefix":"10.1186","volume":"13","author":[{"given":"Sonal","family":"Kothari","sequence":"first","affiliation":[]},{"given":"John H","family":"Phan","sequence":"additional","affiliation":[]},{"given":"Andrew N","family":"Young","sequence":"additional","affiliation":[]},{"given":"May D","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2013,3,13]]},"reference":[{"issue":"4","key":"173_CR1","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3322\/caac.20121","volume":"61","author":"R Siegel","year":"2011","unstructured":"Siegel R, Ward E, Brawley O, Jemal A: Cancer statistics, 2011. 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