Histological image classification using biologically interpretable shape-based features

Abstract

BackgroundAutomatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. 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.MethodsWe 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.ResultsThe 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.ConclusionsShape-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.

Pathology imaging informatics for quantitative analysis of whole-slide images

“…Contour-based features include the properties of shape boundary such as perimeter, boundary fractal dimension, and bending energy. They also include coefficients of parametric shape models such as Fourier shape descriptors and elliptical models 47. Region-based features include area, solidity, and Zernike moments 48.…”

Supremacy of modern morphometry in typing renal oncocytoma and malignant look-alikes

“…Nuclear and cytoplasmic colour features seem to play a major role in the discrimination of this renal cancer type, as the exclusive use of nuclear shape did not yield sufficient discrimination power in a study by (Kim et al 2012). Using shape-based features, (Kothari et al 2013) achieved an accuracy of 77 % in 103 renal tumour samples consisting of ChRCC, CCRCC, RO and the papillary RCC subtype (PRCC). In contrast to Kothari et al, we included broader range morphological characteristics.…”

“…Rather, correct tumour typing relied on the integration of several features. In this context, colour feature registration represents a novel promising advantage of morphometry, as colour features are less well appreciated in the pathologist's routine work (Kothari et al 2013). However, not all features visible by microscopy could well be translated into digital commands, such as cytoplasmic granularity, nuclear grooves and nuclear inclusions.…”

Automated Histology Analysis: Opportunities for signal processing

“…As such, most approaches follow the paradigm of feature extraction followed by classification. The authors in [52] address the problem of classifying subtypes of renal tumor in expertselected ROIs. They use Fourier shape descriptors extracted from binary masks of nuclei, cytoplasm, and unstained regions as features and a series of SVM classifiers arranged in a directed acyclic graph to distinguish between three types of renal cell carcinoma and one benign tumor.…”