Multi-Field-of-View Framework for Distinguishing Tumor Grade in ER+ Breast Cancer From Entire Histopathology Slides

Abstract

Modified Bloom–Richardson (mBR) grading is known to have prognostic value in breast cancer (BCa), yet its use in clinical practice has been limited by intra- and interobserver variability. The development of a computerized system to distinguish mBR grade from entire estrogen receptor-positive (ER+) BCa histopathology slides will help clinicians identify grading discrepancies and improve overall confidence in the diagnostic result. In this paper, we isolate salient image features characterizing tumor morphology and texture to differentiate entire hematoxylin and eosin (H and E) stained histopathology slides based on mBR grade. The features are used in conjunction with a novel multifield-of-view (multi-FOV) classifier—a whole-slide classifier that extracts features from a multitude of FOVs of varying sizes—to identify important image features at different FOV sizes. Image features utilized include those related to the spatial arrangement of cancer nuclei (i.e., nuclear architecture) and the textural patterns within nuclei (i.e., nuclear texture). Using slides from 126 ER+ patients (46 low, 60 intermediate, and 20 high mBR grade), our grading system was able to distinguish low versus high, low versus intermediate, and intermediate versus high grade patients with area under curve values of 0.93, 0.72, and 0.74, respectively. Our results suggest that the multi-FOV classifier is able to 1) successfully discriminate low, medium, and high mBR grade and 2) identify specific image features at different FOV sizes that are important for distinguishing mBR grade in H and E stained ER+ BCa histology slides.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

“…69 Texture features could describe the variation in chromatin distribution, which is generally more heterogeneous in higher grade BC cells. 89 And nuclear densitometric features were significantly associated with nuclear grading. 90 In addition, the spatial arrangement of cell nuclei is important in distinguishing between differentiation degrees.…”

Localization of Diagnostically Relevant Regions of Interest in Whole Slide Images: a Comparative Study

“…Histopathological image analysis research tackles many problems related to diagnosis of the disease, including nucleus detection [3][4][5][6][7], prediction of clinical variables (diagnosis [8][9][10][11][12], grade [13][14][15][16][17][18], survival time [19][20][21]), identification of genetic factors controlling tumor morphology (gene expression [20,22], molecular subtypes [20,23]), and localization of ROIs [24][25][26][27][28]. One of the major research directions in histopathological image analysis is to develop image features for different problems and image types.…”

“…One of the major research directions in histopathological image analysis is to develop image features for different problems and image types. Commonly used image features include low-level features (color [9,10,15,16,18,21,[27][28][29][30][31], texture [10-14, 18, 28]), object level features (shape [32][33][34][35][36][37], topology [8,11,14,18,26,31]), and semantic features (statistics [19,26], histograms [28,32], bag-of-words [28]). …”

Multi-Scale Deep Neural Network for Mitosis Detection in Breast Cancer Histological Images

“…In hematoxylin and eosin (H&E) stained breast cancer sections, mitoses are discernible as hyperchromatic objects with dark color that lack clear nuclear membranes and have irregularity shape properties. In fact, mitosis is a complex process during which a cell nucleus undergoes four phase and exhibits highly variable appearance, moreover in most stages a mitotic nucleus looks like a non-mitotic nucleus shown in Fig.1 [2]. Therefore, the identification of mitosis may often suffer from disagreement between inter-observers.…”