Breast Fibroadenoma: Mapping of Pathophysiologic Features with Three-Time-Point, Contrast-enhanced MR Imaging—Pilot Study

Weinstein, Daphna; Strano, Shalom; Cohen, Patricia; Fields, Scott; Gomori, J. Moshe; Degani, Hadassa

doi:10.1148/radiology.210.1.r99ja18233

Cited by 62 publications

(47 citation statements)

References 30 publications

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“…It was noted previously that the small size of fibroblastic cells, as well as the large interstitial space, resulted in a large extracellular volume fraction. 50 Indeed, the values we found for the extracellular volume fraction confirmed this trend. However, from a diagnostic aspect, the ROC analysis of these lesions yielded only 73% sensitivity and specificity.…”

Section: Discussionsupporting

confidence: 80%

“…This discrepancy may be explained by the occurrence of two different types of fibroadenomas: "old," which consists of sclerotic regions with low vascular permeability and low density of vessels, and "young," consisting of highly proliferative, more permeable, and dense capillaries. 50 Indeed, the high heterogeneity in the transcapillary transfer and rate constants observed in our study also may have resulted from the inclusion of both "old" and "young" fibroadenomas in one group.…”

Section: Discussionmentioning

confidence: 70%

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Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

Furman‐Haran

Schechtman

Kelcz

et al. 2005

Cancer

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BACKGROUNDTumor perfusion through the microvascular network can be imaged noninvasively by dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). The objective of the current study was to quantify the microvascular perfusion parameters in various human breast lesions and to determine whether they varied between benign lesions and malignancy and whether they were altered with increased invasiveness.METHODSPerfusion parameters in 22 benign fibrocystic changes, 15 ductal carcinomas in situ (DCIS), 30 infiltrating ductal carcinomas (IDC), and 22 fibroadenomas were measured using high‐resolution DCE‐MRI. Pixel‐by‐pixel image analysis yielded parametric images of two perfusion indicators: the influx transcapillary transfer constant (ktrans) and the efflux transcapillary rate constant (kep). Correlations of lesion type and perfusion parameters were calculated using Spearman correlation. Logistic regression analysis evaluated the best predictors of the kinetic parameters that differentiate between IDC and benign lesions.RESULTSThe perfusion parameters exhibited a progressive increase from benign fibrocystic changes to DCIS and IDC, with a significant correlation between lesion type and the parameters' values (range of correlation coefficients, 0.56–0.76; P < 0.0001). In addition, ktrans increased from low‐grade DCIS to high‐grade DCIS. Fibroadenomas were characterized uniquely by high ktrans but low kep. Stepwise logistic regression selected ktrans as the best predictor for distinguishing benign fibrocystic changes from IDC, yielding 93% sensitivity and 96% specificity.CONCLUSIONSThe microvascular perfusion parameters in breast lesions were elevated with invasiveness. Quantification of these parameters using high‐resolution DCE‐MRI was helpful for differentiating between breast lesions and should improve breast carcinoma diagnosis. Cancer 2005. © 2005 American Cancer Society.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 70%

Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

Furman‐Haran

Schechtman

Kelcz

et al. 2005

Cancer

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“…For visual inspection, the time-series signals are typically displayed as relative-enhancement curves depicting the signal enhancement with respect to the signal intensity in the precontrast image. Model-based 3TP method [14,15] as the basis for pseudo-coloring of lesions voxels: the intensity of the pseudo-color represents the amount of signal uptake between the precontrast and the early postcontrast image. The presence or absence of a wash-out is associated with the color hue, yielding thus a simple lesions' evaluation method which is capable to integrate information from exactly one precontrast and two postcontrast images.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning

Twellmann

Meyer‐Baese

Lange

et al. 2008

Engineering Applications of Artificial Intelligence

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Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important tool in breast cancer diagnosis, but evaluation of multitemporal 3D image data holds new challenges for human observers. To aid the image analysis process, we apply supervised and unsupervised pattern recognition techniques for computing enhanced visualizations of suspicious lesions in breast MRI data. These techniques represent an important component of future sophisticated computer-aided diagnosis (CAD) systems and support the visual exploration of spatial and temporal features of DCE-MRI data stemming from patients with confirmed lesion diagnosis. By taking into account the heterogeneity of cancerous tissue, these techniques reveal signals with malignant, benign and normal kinetics. They also provide a regional subclassification of pathological breast tissue, which is the basis for pseudo-color presentations of the image data. Intelligent medical systems are expected to have substantial implications in healthcare politics by contributing to the diagnosis of indeterminate breast lesions by non-invasive imaging.

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“…The classification results were compared with the results of manual lesion segmentation done using the model-based threetime-point technique (11). A performance analysis showed that the classifier produced a receiver operating characteristics (ROC) curve with an area under the curve (A z ) of 0.986 for malignant tissues.…”

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confidence: 99%

Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data

Woods

Clymer

Kurç

et al. 2007

Magnetic Resonance Imaging

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Purpose:To investigate the use of four-dimensional (4D) co-occurrence-based texture analysis to distinguish between nonmalignant and malignant tissues in dynamic contrast-enhanced (DCE) MR images. Materials and Methods:4D texture analysis was performed on DCE-MRI data sets of breast lesions. A modelfree neural network-based classification system assigned each voxel a "nonmalignant" or "malignant" label based on the textural features. The classification results were compared via receiver operating characteristic (ROC) curve analysis with the manual lesion segmentation produced by two radiologists (observers 1 and 2). Results:The mean sensitivity and specificity of the classifier agreed with the mean observer 2 performance when compared with segmentations by observer 1 for a 95% confidence interval, using a two-sided t-test with ␣ ϭ 0.05. The results show that an area under the ROC curve (A z ) of 0.99948, 0.99867, and 0.99957 can be achieved by comparing the classifier vs. observer 1, classifier vs. union of both observers, and classifier vs. intersection of both observers, respectively. Conclusion:This study shows that a neural network classifier based on 4D texture analysis inputs can achieve a performance comparable to that achieved by human observers, and that further research in this area is warranted.

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Breast Fibroadenoma: Mapping of Pathophysiologic Features with Three-Time-Point, Contrast-enhanced MR Imaging—Pilot Study

Cited by 62 publications

References 30 publications

Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

Magnetic resonance imaging reveals functional diversity of the vasculature in benign and malignant breast lesions

Model-free visualization of suspicious lesions in breast MRI based on supervised and unsupervised learning

Malignant‐lesion segmentation using 4D co‐occurrence texture analysis applied to dynamic contrast‐enhanced magnetic resonance breast image data

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