A new algorithm for automatic vascular mapping of DCE-MRI of the breast: Clinical application of a potential new biomarker.

BACKGROUND AND OBJECTIVE: Vascularity evaluation on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a potential diagnostic value, but it represents a time consuming procedure, affected by intra- and inter-observer variability. This study tests the application of a recently published method to reproducibly quantify breast vascularity, and evaluates if the vascular volume of cancer-bearing breast, calculated from automatic vascular maps (AVMs), may correlate with pathologic tumor response after neoadjuvant chemotherapy (NAC). METHODS: Twenty-four patients with unilateral locally advanced breast cancer underwent DCE-MRI before and after NAC, 8 responders and 16 non-responders. A validated algorithm, based on multiscale 3D Hessian matrix analysis, provided AVMs and allowed the calculation of vessel volume before the initiation and after the last NAC cycle for each breast. For cancer bearing breast, the difference in vascular volume before and after NAC was compared in responders and non-responders using the Wilcoxon two-sample test. A radiologist evaluated the vascularity on the subtracted images (first enhanced minus unenhanced), before and after treatment, assigning a vascular score for each breast, according to the number of vessels with length ≥30mm and maximal transverse diameter ≥2mm. The same evaluation was repeated with the support of the simultaneous visualization of the AVMs. The two evaluations were compared in terms of mean number of vessels and mean vascular score per breast, in responders and non-responders, by use of Wilcoxon two sample test. For all the analysis, the statistical significance level was set at 0.05. RESULTS: For breasts harboring the cancer, evidence of a difference in vascular volume before and after NAC for responders (median=1.71cc) and non-responders (median=0.41cc) was found (p=0.003). A significant difference was also found in the number of vessels (p=0.03) and vascular score (p=0.02) before or after NAC, according to the evaluation supported by the AVMs. CONCLUSIONS: The encouraging, although preliminary, results of this study suggest the use of AVMs as new biomarker to evaluate the pathologic response after NAC, but also support their application in other breast DCE-MRI vessel analysis that are waiting for a reliable quantification method.

Contrast-enhanced MR imaging of the breast: association between asymmetric increased breast vascularity and ipsilateral cancer in a consecutive series of 197 patients.

PURPOSE: This study was done to estimate the diagnostic performance of an asymmetric increase in breast vascularity (AIBV) for ipsilateral cancer. MATERIALS AND METHODS: A total of 197 patients without previous breast interventions underwent bilateral contrast-enhanced (gadoterate meglumine, 0.1 mmol/kg) magnetic resonance (MR) imaging. Vessels >-2 mm in diameter and ≥ 3 cm in length were counted on maximum intensity projections: a difference ≥ 2 in number between the two breasts was considered AIBV. Pathology or ≥ 1 year follow-up served as a reference standard. The difference in sensitivity of AIBV between invasive and ductal carcinoma in situ (DCIS) as well as the association between AIBV and the diameter of invasive lesions or the histological grade were evaluated using χ(2) test. RESULTS: Pathology revealed 82 malignancies and 20 benign lesions: 70 invasive carcinomas (57 ductal, nine lobular, three mucinous, one papillary) and 12 DCIS: 10 fibroadenomas, two papillomas, two atypical ductal hyperplasias and six other benign lesions. The remaining 95 patients were negative at follow-up. Sensitivity of AIBV was 74% (61/82), specificity 94% (108/115), accuracy 86% (169/197), positive predictive value 90% (61/68) and negative predictive value 84% (108/129). Sensitivity for invasive cancers (80%; 56/70) was significantly higher than that for DCIS (42%; 5/12) (p<0.001). For invasive cancers, sensitivity was 40% (2/5) for lesions ≤ 9 mm in diameter, 69% (9/13) for those 10-14 mm, 79% (15/19) for those 15-19 mm and 91% (30/33) for those ≥ 20 mm (p<0.001). The G3 lesion rate was 49% (27/55) among true positives and only 7% (1/14) among false negatives (p=0.009). CONCLUSIONS: An association between AIBV and ipsilateral cancer exists, particularly for invasive cancers ≥ 20 mm or with high pathologic grade.

Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features.

PURPOSE: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features. METHODS: Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals. RESULTS: SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 ± 0.04 (mean ± standard deviation), 0.87 ± 0.06, and 0.86 ± 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 ± 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features. CONCLUSIONS: Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately.