Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach
Open Access
- 25 April 2017
- journal article
- research article
- Published by Springer Science and Business Media LLC in Scientific Reports
- Vol. 7 (1), 46732
- https://doi.org/10.1038/srep46732
Abstract
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.This publication has 38 references indexed in Scilit:
- SLIT/ROBO1 Signaling Suppresses Mammary Branching Morphogenesis by Limiting Basal Cell NumberDevelopmental Cell, 2011
- Evaluation of the prognostic significance of MSMB and CRISP3 in prostate cancer using automated image analysisLaboratory Investigation, 2011
- Towards a knowledge-based Human Protein AtlasNature Biotechnology, 2010
- An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patientsBreast Cancer Research and Treatment, 2009
- SLITs Suppress Tumor Growth In vivo by Silencing Sdf1/Cxcr4 within Breast EpitheliumCancer Research, 2008
- Regulation of In Situ to Invasive Breast Carcinoma TransitionCancer Cell, 2008
- Myoepithelial Cells: Any role in aspiration cytology smears of breast tumors?Cytojournal, 2008
- Distribution and significance of 14‐3‐3σ, a novel myoepithelial marker, in normal, benign, and malignant breast tissueThe Journal of Pathology, 2004
- The human myoepithelial cell displays a multifaceted anti-angiogenic phenotypeOncogene, 2000
- The Human Myoepithelial Cell Exerts Antiproliferative Effects on Breast Carcinoma Cells Characterized by p21WAF1/CIP1Induction, G2/M Arrest, and ApoptosisExperimental Cell Research, 1998