Detection and classification of contrast‐enhancing masses by a fully automatic computer‐assisted diagnosis system for breast MRI

Abstract

Purpose: To evaluate a fully automatic computer-assisted diagnosis (CAD) method for breast magnetic resonance imaging (MRI), which considered dynamic as well as morphologic parameters and linked those to descriptions laid down in the Breast Imaging Reporting and Data System (BI-RADS) MRI atlas.Materials and Methods: MR images of 108 patients with 141 histologically proven mass-like lesions (88 malignant, 53 benign) were included. The CAD system automatically performed the following processing steps: 3D nonrigid motion correction, detection of lesions by a segmentation algorithm, extraction of multiple dynamic and morphologic parameters, and classification of lesions. As one final result, the lesions were categorized by defining their probability of malignancy; this so-called morpho-dynamic index (MDI) ranged from 0%-100%. The results of the CAD system were correlated with histopathologic findings. Results:The CAD system had a high detection rate of the histologically proven lesions, missing only two malignancies of invasive multifocal carcinomas and four benign lesions (three fibroadenomas, one atypical ductal hyperplasia). The 86 detected malignant lesions showed a mean MDI of 86.1% (615.4%); the mean MDI of the 49 coded benign lesions was 41.8% (622.0%; P < 0.001). Based on receiver-operating characteristic analysis, the diagnostic accuracy of the CAD system was 93.5%. Using an appropriate cutoff value (MDI 50%), sensitivity was 96.5% combined with specificity of 75.5%. Conclusion:The fully automatic CAD technique seems to reliably distinguish between benign and malignant masslike breast tumors. Observer-independent CAD may be a promising additional tool for the interpretation of breast MRI in the clinical routine.

Computer-aided detection of breast lesions in DCE-MRI using region growing based on fuzzy C-means clustering and vesselness filter

“…Among the mentioned studies, only GubernMérida et al [14] and Chang et al [11] present their results based on FROC analysis. Vignati et al [10] and Renz et al [9] do not evaluate the performance of their methods on non-mass-like enhancement lesions. Chang et al [11] and Ertas et al [8] do not clearly express whether their evaluations are performed on non-mass-like lesions.…”

“…The method developed by Ertas et al [8] segments breast regions using a cellular neural network and detects lesions by performing 3D template matching on the normalized maximum intensity-time ratio maps. Renz et al [9] suggest applying a hierarchical 3D Gaussian pyramid method to segment breast lesions. Vignati et al [10] discover breast lesions using a normalization technique based on the contrast-uptake of mammary vessels.…”

Comparison of Gadoteric Acid and Gadobutrol for Detection as Well as Morphologic and Dynamic Characterization of Lesions on Breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging

“…Another important inclusion criterion was the presence of mass breast lesions, as the used CAD system has been solely evaluated in detail for mass lesions and the morphologic description of lesions by the CAD system corresponded to the BI-RADS categories of masses. 18 The reference standard was the histopathologic confirmation of the mass lesions, whereas experienced breast pathologists performed histopathologic diagnosis. The lesions were categorized by using the World Health Organization classification of malignant and benign breast tumors; the grading of carcinomas was specified as well (grade, G1), moderately (G2), and poorly (G3) differentiated cancers.…”

“…The specific characteristic of the used CAD technique was that it automatically detected contrast-enhancing breast lesions and performed dynamic as well as morphologic characterization of the lesions and linked these descriptions to features, laid down in the BI-RADS MRI atlas. 11,18 The CAD system has been recently described and evaluated in detail, showing that it can reliably distinguish between benign and malignant breast mass lesions with a diagnostic accuracy of 93.5%. 18 Possible motion artifacts were reduced by a 3D registration algorithm; contrast-enhancing lesions were detected by a segmentation technique with an adaptive threshold.…”

“…11,18 The CAD system has been recently described and evaluated in detail, showing that it can reliably distinguish between benign and malignant breast mass lesions with a diagnostic accuracy of 93.5%. 18 Possible motion artifacts were reduced by a 3D registration algorithm; contrast-enhancing lesions were detected by a segmentation technique with an adaptive threshold. 18 For morphologic characterization of detected lesions, the CAD system extracted 13 parameters: volume, maximum and minimum diameter, surface area, surface-to-volume ratio, compactness, characteristic of margin, homogeneity, average signal intensity, deviation of signal intensities, presence of spiculations, adjacent vessels, or central necrosis.…”