[en] Objective: To investigate the value of kinetic features measured by computer-aided diagnosis (CAD) for breast MRI. Methods: One hundred and sixty four lesions diagnosed pathologically by operation or biopsy comprised the analysis set. Automated lesion kinetic information from CADStream programs for breast MRI was identified. Three CAD variables were compared for benign and malignant lesions: initial phase peak enhancement (greatest percentage of signal intensity increase on first contrast enhanced sequence), delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement (washout, plateau, or persistent), and delayed phase enhancement categorized by single most suspicious type of kinetics (any washout > any plateau > any persistent). Morphological characteristics of breast lesions were described according to breast imaging and reporting data system (BI-RADS). Initial phase peak enhancement mean values between benign and malignant breast lesions were compared by using Wilcoxon rank-sum test, delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement or by single most suspicious type of kinetics between benign and malignant breast lesions were compared by using Chi-square test. Results: There were 72 benign and 92 malignant breast lesions. A total of 123 (75.0%) mass lesions were identified,and the other 41 (25.0%) lesions showed no mass. Thirty lesions were BI-RADS-MRI 2, 68 lesions were BI-RADS-MRI 3, 43 lesions were BI-RADS-MRI 4, 23 lesions were BI-RADS-MRI 5. Initial phase peak enhancement mean values of benign and malignant lesions were 237% (69% to 629%) and 336% (86% to 793%), respectively. There was no significant difference between benign and malignant lesions in initial peak enhancement mean value (Z=-1.626, P=0.104). Delayed phase enhancement categorized by single most suspicious type of kinetics (any washout > any plateau > any persistent) for benign and malignant lesions were 15, 10, 47 and 2, 3, 87 respectively. There was a significant difference between benign and malignant lesions (χ"2=23.562, P=0.000). Initial peak enhancement value < 100% or ≥100% were 5 and 67 for benign lesions, 3 and 89 for malignant lesions, respectively. There was no significant difference between benign and malignant lesions at 100% threshold (χ"2=1.181, P=0.277). Delayed phase enhancement categorized by a single type of kinetics comprising the largest percentage of enhancement (washout, plateau, or persistent) for benign and malignant lesions were 48, 6, 18 and 47, 15, 30 respectively.There was no significant difference between benign and malignant lesions (χ"2=4.496, P=0.106). Conclusions: Of CAD kinetics analyzed,only delayed enhancement categorized by most suspicious type is helpful for the differentiation between benign and malignant lesions. However, there is significant overlap between initial peak enhancement at 100% threshold or delayed kinetics categorized by largest percentage enhancement types of benign and malignant lesions, so lesion morphologic features should be considered. (authors)

[en] Purpose: To develop and evaluate a computerized semiautomatic segmentation method for accurate extraction of three-dimensional lesions from dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) of the breast. Methods: The authors propose a new background distribution-based active contour model using level set (BDACMLS) to segment lesions in breast DCE-MRIs. The method starts with manual selection of a region of interest (ROI) that contains the entire lesion in a single slice where the lesion is enhanced. Then the lesion volume from the volume data of interest, which is captured automatically, is separated. The core idea of BDACMLS is a new signed pressure function which is based solely on the intensity distribution combined with pathophysiological basis. To compare the algorithm results, two experienced radiologists delineated all lesions jointly to obtain the ground truth. In addition, results generated by other different methods based on level set (LS) are also compared with the authors’ method. Finally, the performance of the proposed method is evaluated by several region-based metrics such as the overlap ratio. Results: Forty-two studies with 46 lesions that contain 29 benign and 17 malignant lesions are evaluated. The dataset includes various typical pathologies of the breast such as invasive ductal carcinoma, ductal carcinomain situ, scar carcinoma, phyllodes tumor, breast cysts, fibroadenoma, etc. The overlap ratio for BDACMLS with respect to manual segmentation is 79.55% ± 12.60% (mean ± s.d.). Conclusions: A new active contour model method has been developed and shown to successfully segment breast DCE-MRI three-dimensional lesions. The results from this model correspond more closely to manual segmentation, solve the weak-edge-passed problem, and improve the robustness in segmenting different lesions