Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach

Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuc...

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Bibliographic Details
Published in:Scientific reports 2017-04, Vol.7 (1), p.46732-46732, Article 46732
Main Authors: Yamamoto, Yoichiro, Saito, Akira, Tateishi, Ayako, Shimojo, Hisashi, Kanno, Hiroyuki, Tsuchiya, Shinichi, Ito, Ken-ichi, Cosatto, Eric, Graf, Hans Peter, Moraleda, Rodrigo R., Eils, Roland, Grabe, Niels
Format: Article
Language:English
Subjects:
13/51
14/28
631/114/1564
692/308/575
Artificial intelligence
Breast cancer
Computer applications
Electron microscopy
Humanities and Social Sciences
Hyperplasia
Invasiveness
Learning algorithms
Mammography
multidisciplinary
Nuclei
Paracrine signalling
Science
Tumor cells
Tumors
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Summary:Machine learning systems have recently received increased attention for their broad applications in several fields. In this study, we show for the first time that histological types of breast tumors can be classified using subtle morphological differences of microenvironmental myoepithelial cell nuclei without any direct information about neoplastic tumor cells. We quantitatively measured 11661 nuclei on the four histological types: normal cases, usual ductal hyperplasia and low/high grade ductal carcinoma in situ (DCIS). Using a machine learning system, we succeeded in classifying the four histological types with 90.9% accuracy. Electron microscopy observations suggested that the activity of typical myoepithelial cells in DCIS was lowered. Through these observations as well as meta-analytic database analyses, we developed a paracrine cross-talk-based biological mechanism of DCIS progressing to invasive cancer. Our observations support novel approaches in clinical computational diagnostics as well as in therapy development against progression.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep46732