ArticleJournal of pathology informatics2016
Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.
Article in Journal of pathology informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Macrophages in Giemsa-stained cerebrospinal fluid specimens predict carcinomatous meningitis.Oncology letters · 2020Article
- Morphological Estimation of Cellularity on Neo-Adjuvant Treated Breast Cancer Histological Images.Journal of imaging · 2020Article
- Image analysis of the nuclear characteristics of emerin protein and the correlation with nuclear grooves and intranuclear cytoplasmic inclusions in lung adenocarcinoma.Oncology reports · 2019Article
- Automatic cellularity assessment from post-treated breast surgical specimens.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2017Article
- Nuclear features of infiltrating urothelial carcinoma are distinguished from low-grade noninvasive papillary urothelial carcinoma by image analysis.Oncology letters · 2017Article
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8 authors at 3 institutions in 1 country.
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Abstract
backgroundIntraductal 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.
methodsWe 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.
resultsHigh 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.
conclusionsIn 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.
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