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Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value

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  • Lin-Wei Wang
  • Ai-Ping Qu
  • Jing-Ping Yuan
  • Chuang Chen
  • Sheng-Rong Sun
  • Ming-Bai Hu
  • Juan Liu
  • Yan Li

Abstract

Background: The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis. Methods: Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied. Results: There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve

Suggested Citation

  • Lin-Wei Wang & Ai-Ping Qu & Jing-Ping Yuan & Chuang Chen & Sheng-Rong Sun & Ming-Bai Hu & Juan Liu & Yan Li, 2013. "Computer-Based Image Studies on Tumor Nests Mathematical Features of Breast Cancer and Their Clinical Prognostic Value," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.
    • Handle: RePEc:plo:pone00:0082314
    DOI: 10.1371/journal.pone.0082314
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