Journal of Digital Imaging

, 24:764 | Cite as

Characterizing the Clustered Microcalcifications on Mammograms to Predict the Pathological Classification and Grading: A Mathematical Modeling Approach

  • Yuan-Zhi Shao
  • Li-Zhi Liu
  • Meng-Jie Bie
  • Chan-chan Li
  • Yao-pan Wu
  • Xiao-ming Xie
  • Li Li
Article

Abstract

In this study, we explore a mathematical model to characterize the clustered microcalcifications on mammograms for predicting the pathological classification and grading. Our database consists of both retrospective cases (78 cases) and prospective cases (31 cases) with pathologically diagnosed clusters of microcalcifications on mammograms. The microcalcifications were divided into four grades: grade 0, benign breast disease including mastopathies (n = 12) and fibroadenomas (n = 20); grade 1, well-differentiated infiltrating ductal carcinoma (n = 12); grade 2, moderately differentiated infiltrating ductal carcinoma (n = 38); grade 3, poorly differentiated infiltrating ductal carcinoma (n = 27). A feature parameter, defined as the pattern form factor of microcalcification cluster θ by us, combines five computer-extracted image parameters of microcalcification clusters of those mammograms. In every case, only one imaging was selected for modeling analysis. A total of 109 imagings were adopted in current study. We find the existence of a positive relationship between the feature parameter θ and pathological grading G of microcalcifications in retrospective cases, which was expressed as G =6.438 + 1.186 ×Ln <θ>. The model above has been verified further by the prospective study with a comparative evaluation accuracy of approximately 77.42%. The binary predication simply for both benignancy and malignancy was also included using same but reshuffled data, and the receiver operating characteristic (ROC) analysis was performed with ROC value 0.74351∼0.79891. As one candidate for feature parameter in computer-aided diagnosis, the pattern form factor θ of clustered microcalcifications may be useful to predict the pathological grading and classification of microcalcification clusters on mammography in breast cancer.

Keywords

Algorithms computer-aided diagnosis (CAD) mammography CAD breast diseases clustered microcalcification detection 

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Copyright information

© Society for Imaging Informatics in Medicine 2011

Authors and Affiliations

  • Yuan-Zhi Shao
    • 2
  • Li-Zhi Liu
    • 1
  • Meng-Jie Bie
    • 2
  • Chan-chan Li
    • 1
  • Yao-pan Wu
    • 1
  • Xiao-ming Xie
    • 3
  • Li Li
    • 1
  1. 1.State Key Laboratory of Oncology in Southern China, Imaging Diagnosis and Interventional Center, Cancer CenterSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  2. 2.Department of PhysicsSun Yat-sen UniversityGuangzhouPeople’s Republic of China
  3. 3.State Key Laboratory of Oncology in Southern China, Breast Department, Cancer CenterSun Yat-sen UniversityGuangzhouPeople’s Republic of China

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