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

• Published in: IEEE transactions on biomedical engineering Vol. 60; no. 8; pp. 2089 - 2099
• Main Authors: Basavanhally, Ajay, Ganesan, Shridar, Feldman, Michael, Shih, Natalie, Mies, Carolyn, Tomaszewski, John, Madabhushi, Anant
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Breast cancer (BCa); Breast Neoplasms - metabolism; Breast Neoplasms - pathology; Cancer; digital pathology; Educational institutions; Erbium; Estrogens; Feature extraction; Female; Histology; Histopathology; Humans; image analysis; Image color analysis; Image edge detection; Image Interpretation, Computer-Assisted - methods; Microscopy - methods; modified Bloom-Richardson (mBR) grade; multi-field-of-view (multi-FOV); Neoplasm Grading; nuclear architecture; nuclear texture; Pattern Recognition, Automated - methods; Receptors, Estrogen - metabolism; Reproducibility of Results; Sensitivity and Specificity; Tumors
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2013.2245129

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 Hand E stained ER+ BCa histology slides.