Quantitative morphometric analysis of breast cancer invasion using a machine learning approach

2017 Fiscal Year Final Research Report

• Project/Area Number: 15K18428
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Tumor diagnostics
• Research Institution: Institute of Physical and Chemical Research (2017); Shinshu University (2015-2016)
• Principal Investigator: Yamamoto Yoichiro 国立研究開発法人理化学研究所, 革新知能統合研究センター, ユニットリーダー (00573247)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: 人工知能 / 機械学習 / 乳癌 / 浸潤 / 形態情報

Outline of Final Research Achievements

We found that histological types of breast tumors could 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.

Free Research Field

病理学、情報学