title: Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.
abstract: BACKGROUND
  Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast.
  
  
  METHODS
  We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine.
  
  
  RESULTS
  High diagnostic accuracy (81.8-99.3%) was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance.
  
  
  CONCLUSIONS
  In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.
authors.affiliation: Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan.
authors.forename: Masatoshi
authors.initials: M
authors.lastname: Yamada
authors.affiliation: Department of Quantitative Pathology and Immunology, Tokyo Medical University, Tokyo, Japan.
authors.forename: Akira
authors.initials: A
authors.lastname: Saito
authors.affiliation: Department of Pathology, School of Medicine, Shinshu University, Nagano, Japan.
authors.forename: Yoichiro
authors.initials: Y
authors.lastname: Yamamoto
authors.affiliation: Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Cosatto
authors.affiliation: Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan.
authors.forename: Atsushi
authors.initials: A
authors.lastname: Kurata
authors.affiliation: Department of Human Pathology, Tokyo Medical University, Tokyo, Japan.
authors.forename: Toshitaka
authors.initials: T
authors.lastname: Nagao
authors.affiliation: Department of Laboratory Medicine, Shinshu University Hospital, Nagano, Japan.
authors.forename: Ayako
authors.initials: A
authors.lastname: Tateishi
authors.affiliation: Department of Molecular Pathology, Tokyo Medical University, Tokyo, Japan.
authors.forename: Masahiko
authors.initials: M
authors.lastname: Kuroda
keywords: Intraductal proliferative lesion of breast; nucleic analysis; whole slide imaging