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Computerized morphometry analysis of epithelial fimbriae nuclear symmetry in BRCA carriers may identify patients at risk for developing ovarian cancer
Authors:Amnon Amit  Edmond Sabo  Anna Petruseva  Leah Stroller  Ari Reiss  Geula Klorin
Affiliation:1. Department of Gynecology–Oncology, Rambam Health Care Campus, Haifa, Israel

Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel;2. Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

Department of Pathology, Carmel Medical Center, Haifa, Israel;3. Department of Gynecology–Oncology, Rambam Health Care Campus, Haifa, Israel;4. Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel

Abstract:Serous ovarian tumors may originate in epithelial cells of the fallopian tubes. Computerized morphometry was able to find significant alterations in the fallopian tube epithelium of healthy BRCA carriers. The purpose of this study was to identify a subgroup of BRCA carriers that may be at risk of developing serous ovarian cancer by evaluation of the epithelial nuclear symmetry in the fallopian tubes. Four groups of patients were analyzed; healthy patients, ovarian cancer patients, BRCA carriers, and BRCA noncarriers. All fallopian tubes appeared normal by H&E examination. The ImageProPlus software was used to assess the nuclear symmetry of 65 fimbriae epithelium cells and an artificial neural network algorithm aided in detecting a subpopulation among fimbriae of healthy BRCA carriers at risk for ovarian cancer. Significant differences were found between healthy patients and ovarian cancer patients, and between BRCA carriers and noncarriers. The algorithm was able to accurately predict BRCA carriers with associated ovarian cancer based on fallopian tube nuclear symmetry characteristics. These results reinforce the hypothesis that fimbriae epithelial cells of BRCA carriers may undergo early-stage changes that could predict the risk of progression toward malignancy.
Keywords:computerized morphometry  fallopian tubes  neural network  nuclear symmetry  ovarian cancer
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