This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast...This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images.D(,) is the diagnostic parameter derived from the logistic model.Significant differences were found in D(,) between the malignant benign groups.Fisher's Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(,)).Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(,)) indicated high sensitivity and specificity to differentiate malignancy from benignancy.The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR.Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(,) as the lesion's feature.The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.展开更多
文摘This study proposes a novel dual S-shaped logistic model for automatically quantifying the characteristic kinetic curves of breast lesions and for distinguishing malignant from benign breast tumors on dynamic contrast enhanced (DCE) magnetic resonance (MR) images.D(,) is the diagnostic parameter derived from the logistic model.Significant differences were found in D(,) between the malignant benign groups.Fisher's Linear Discriminant analysis correctly classified more than 90% of the benign and malignant kinetic breast data using the derived diagnostic parameter (D(,)).Receiver operating characteristic curve analysis of the derived diagnostic parameter (D(,)) indicated high sensitivity and specificity to differentiate malignancy from benignancy.The dual S-shaped logistic model was effectively used to fit the kinetic curves of breast lesions in DCE-MR.Separation between benign and malignant breast lesions was achieved with sufficient accuracy by using the derived diagnostic parameter D(,) as the lesion's feature.The proposed method therefore has the potential for computer-aided diagnosis in breast tumors.
