Affiliations: Department of Machine Learning, NEC Laboratories America, NJ, USA | Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA | Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan | Department of Pathology, St. Luke's Hospital (St. Louis), Chesterfield, MO, USA | Innovative Service Solutions Division, NEC Corporation, Tokyo, Japan
Note: [] Corresponding author: Christopher Malon, PhD, Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Princeton, NJ 08540, USA. Tel.: +1 609 951 2594; Fax: +1 609 951 2482; E-mail: [email protected]
Abstract: Despite the prognostic importance of mitotic count as one of the components of the Bloom - Richardson grade [3], several studies ([2, 9, 10]) have found that pathologists' agreement on the mitotic grade is fairly modest. Collecting a set of more than 4,200 candidate mitotic figures, we evaluate pathologists' agreement on individual figures, and train a computerized system for mitosis detection, comparing its performance to the classifications of three pathologists. The system's and the pathologists' classifications are based on evaluation of digital micrographs of hematoxylin and eosin stained breast tissue. On figures where the majority of pathologists agree on a classification, we compare the performance of the trained system to that of the individual pathologists. We find that the level of agreement of the pathologists ranges from slight to moderate, with strong biases, and that the system performs competitively in rating the ground truth set. This study is a step towards automatic mitosis count to accelerate a pathologist's work and improve reproducibility.
