Ductal Carcinoma In Situ: Detection, Diagnosis, and Characterization with Magnetic Resonance Imaging
ABSTRACT Ductal carcinoma in situ (DCIS) is a preinvasive malignancy that currently accounts for over 20% of newly diagnosed breast cancers in the US. This article reviews how clinical magnetic resonance imaging methods are being implemented for the detection, diagnosis and characterization of DCIS. Research strategies that are being pursued to help realize the full potential for magnetic resonance imaging to improve the outcomes of patients diagnosed with DCIS are discussed. Semin Ultrasound CT MRI 32:306-318 (c) 2011 Elsevier Inc. All rights reserved.
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ABSTRACT: The purpose of this study is to evaluate the role of MRI for characterization of high-risk breast lesions diagnosed at imaging-guided needle biopsy. In this retrospective analysis of 220 patients, 227 high-risk lesions (94 papillomas, 64 radial sclerosing lesions, 46 lobular neoplasias, and 23 atypical ductal hyperplasias) found at 11-gauge vacuum-assisted or 14-gauge needle biopsy were studied with dynamic MRI (time resolution, 84 or 88 seconds; gadopentetate dimeglumine or gadobenate dimeglumine, 0.1 mmol/kg). When lesions showed contrast enhancement on subtracted images, they were considered suspicious for malignancy. The reference standard was histopathologic examination after surgical excision in 190 of 227 (84%) lesions and negative follow-up (≥ 24 months) in 37 of 227 (16%) lesions. Predictive values and likelihood ratios were calculated. Of 227 lesions, 155 (68%) were contrast enhancing and 72 (32%) were not. Of 155 contrast-enhancing lesions, 28 (18%) were upgraded to malignancy after surgical excision (nine papillomas, one radial sclerosing lesion, 11 lobular neoplasias, and seven atypical ductal hyperplasias); there were 11 invasive carcinomas and 17 ductal carcinomas in situ, four of the latter being G3. Of 72 non-contrast-enhancing lesions, two (3%) were upgraded to malignancy after surgical excision (one radial sclerosing lesion and one lobular neoplasia), both of which were G1 ductal carcinoma in situ. Cancer probability was significantly higher for contrast-enhancing (18%) than for non-contrast-enhancing (3%) lesions (p = 0.001) and for nonmasslike (43%) than for masslike (14%) lesions (p = 0.005). The positive predictive value was 18% (28/155; 95% CI, 13-24%), the negative predictive value was 97% (70/72; 95% CI, 94-99%), the positive likelihood ratio was 1.448 (95% CI, 1.172-1.788), and the negative likelihood ratio was 0.188 (95% CI, 0.152-0.232). The absence of enhancement at dynamic MRI allowed reliable exclusion of invasive cancers among high-risk lesions diagnosed at needle biopsy.American Journal of Roentgenology 08/2012; 199(2):W240-50. DOI:10.2214/AJR.11.7869 · 2.74 Impact Factor
- European Journal of Radiology 09/2012; 81:S189–S191. DOI:10.1016/S0720-048X(12)70078-5 · 2.16 Impact Factor
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ABSTRACT: Non-mass enhancing lesions represent one of the most challenging types of lesions for both the clinician as well as current computer-aided diagnosis (CAD) systems. Differently from the well-studied mass-enhancing tumors these lesions do not exhibit a typical kinetic behavior that can be further easily categorized into benign or malignant based on feature descriptors. Furthermore, the poorly defined tumor borders pose a difficulty to even the most sophisticated segmentation algorithms. To address these challenges in terms of segmentation and atypical contrast enhancement dynamics, we apply an ICA-based segmentation on these lesions and extract from the average signal intensity curve of the most representative independent component (IC). Subsequently the dynamics of this IC is modeled based on mathematical models such as the empirical mathematical model and the phenomenological universalities. An automated computer-aided diagnosis system evaluates the atypical behavior of these lesions, and additionally compares the benefit of ICA-segmentation versus active contour segmentation.SPIE, Baltimore, Maryland USA; 04/2013