title: 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 multi-field-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.
authors: Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854, USA. abasavan@eden.rutgers.edu
authors: Ajay
authors: A
authors: Basavanhally
authors: Shridar
authors: S
authors: Ganesan
authors: Michael
authors: M
authors: Feldman
authors: Natalie
authors: N
authors: Shih
authors: Carolyn
authors: C
authors: Mies
authors: John
authors: J
authors: Tomaszewski
authors: Anant
authors: A
authors: Madabhushi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D060787:Neoplasm Grading; D010363:Pattern Recognition, Automated / Q000379:methods*; D011960:Receptors, Estrogen / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D011960:Receptors, Estrogen
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