title: Characterizing the clustered microcalcifications on mammograms to predict the pathological classification and grading: a mathematical modeling approach.
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.
authors.affiliation: Department of Physics, Sun Yat-sen University, Guangzhou 510275, People's Republic of China.
authors.forename: Yuan-Zhi
authors.initials: YZ
authors.lastname: Shao
authors.forename: Li-Zhi
authors.initials: LZ
authors.lastname: Liu
authors.forename: Meng-Jie
authors.initials: MJ
authors.lastname: Bie
authors.forename: Chan-chan
authors.initials: CC
authors.lastname: Li
authors.forename: Yao-pan
authors.initials: YP
authors.lastname: Wu
authors.forename: Xiao-ming
authors.initials: XM
authors.lastname: Xie
authors.forename: Li
authors.initials: L
authors.lastname: Li
mesh_terms: D001943:Breast Neoplasms / Q000175:diagnosis*; D002114:Calcinosis / Q000145:classification / Q000175:diagnosis* / Q000473:pathology*; D003937:Diagnosis, Differential; D004311:Double-Blind Method; D005260:Female; D006801:Humans; D008327:Mammography; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies