MR Imaging Phenotypes and Features Associated with Pathogenic Mutation to Predict Recurrence or Metastasis in Breast Cancer

Abstract Objectives: To develop a multiple-feature model using clinico-pathological and radiologic characteristics adding pathogenic mutations associated signs for predicting recurrence or metastasis in breast cancers at high familial risk women. Methods: Genetic testing for breast-related gene mutations was performed in 54 patients with breast cancers. Breast MRI findings were retrospectively evaluated in 64 tumors of 54 patients. The relationship between pathogenic mutation and clinico-pathological and radiologic features was examined, and disease recurrence or metastasis was estimated. Multiple logistic regression analyses were performed to identify independent factors of pathogenic mutation and disease recurrence or metastasis. Based on the above significant factors, a multivariate logistic regression was adopted to establish two models for predicting disease recurrence or metastasis in breast cancer by using R software. Results: On the 64 breast cancers, the patients on 17 cancers have pathogenic mutation，and no pathogenic mutation on 47 cancers. The clinico-pathogenic and radiologic features associated with pathogenic mutation included six signs: biologic features ( p= 0.000), nuclear grade ( p= 0.045), breast density ( p= 0.005), MRI lesion type ( p= 0.000), internal enhancement pattern ( p= 0.004), and spiculated margin ( p= 0.049). Necrosis within the tumors was only feature associated with increased disease recurrence or metastasis ( p= 0.006). The modelⅠincluding clinico-pathologic and radiologic factors showed good discrimination in predicting disease recurrence or metastasis. The comprehensive modelⅡ including modelⅠcombined pathogenic mutations significantly associated signs is more effective. Conclusions: The multiple-feature model in addition to pathogenic mutations associated parameters can more effective in predicting disease recurrence or metastasis.