ArticleScientific reports2017
Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 46 citations in OpenAlex.
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- Artificial Intelligence as an Assistive Tool in Breast Cancer Screening and Diagnosis in the Digital Era.International journal of breast cancer · 2026Article
- Use of AI Histopathology in Breast Cancer Diagnosis.Medicina (Kaunas, Lithuania) · 2025Review
- Advances in Breast Cancer Care: The Role of Artificial Intelligence and Digital Pathology in Precision Medicine.European journal of breast health · 2025Article
- Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance.Signal transduction and targeted therapy · 2024Review
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- Artificial Intelligence in Regenerative Medicine: Applications and Implications.Biomimetics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in Histologic Diagnosis of Ductal Carcinoma In Situ.Mayo Clinic proceedings. Digital health · 2023Review
- Automated Classification of Free-Text Radiology Reports: Using Different Feature Extraction Methods to Identify Fractures of the Distal Fibula.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2023Article
- Development of multiple AI pipelines that predict neoadjuvant chemotherapy response of breast cancer using H&E-stained tissues.The journal of pathology. Clinical research · 2023Article
- The development of a machine learning algorithm for early detection of viral hepatitis B infection in Nigerian patients.Scientific reports · 2023Article
- Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation.Cureus · 2023Article
- Artificial intelligence in clinical research of cancers.Briefings in bioinformatics · 2022Article
- A data-driven ultrasound approach discriminates pathological high grade prostate cancer.Scientific reports · 2022Article
- Beyond the In-Practice CBC: The Research CBC Parameters-Driven Machine Learning Predictive Modeling for Early Differentiation among Leukemias.Diagnostics (Basel, Switzerland) · 2022Article
- Improving DCIS diagnosis and predictive outcome by applying artificial intelligence.Biochimica et biophysica acta. Reviews on cancer · 2021Review
- Automated and unbiased discrimination of ALS from control tissue at single cell resolution.Brain pathology (Zurich, Switzerland) · 2021Article
- High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.Scientific reports · 2021Article
- The advent of medical artificial intelligence: lessons from the Japanese approach.Journal of intensive care · 2020Review
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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