Ductal Carcinoma In Situ: Detection, Diagnosis, and Characterization with Magnetic Resonance Imaging

Mouse Cancer Genetics Program, National Cancer Institute, Frederick, MD, USA.
Seminars in Ultrasound CT and MRI (Impact Factor: 1.08). 08/2011; 32(4):306-18. DOI: 10.1053/j.sult.2011.02.007
Source: PubMed

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.

1 Follower
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objectives To identify preoperative features that could be used to predict invasive breast cancer in women with a diagnosis of ductal carcinoma in situ (DCIS) at ultrasound (US)-guided 14-gauge core needle biopsy (CNB). Methods A total of 86 DCIS lesions that were diagnosed at US-guided 14-gauge CNB and excised surgically in 84 women were assessed. We retrospectively reviewed the patients’ medical records, mammography, US, and MR imaging. We compared underestimation rates of DCIS for the collected clinical and radiologic variables and determined the preoperative predictive factors for upstaging to invasive cancer. Results Twenty-seven (31.4%) of 86 DCIS lesions were upgraded to invasive cancer. Preoperative features that showed a significantly higher underestimation of DCIS were palpability or nipple discharge (p = 0.040), number of core specimens less than 5 (p = 0.011), mammographic maximum lesion size of 25 mm or larger (p = 0.022), mammographic mass size of 40 mm or larger (p = 0.046), sonographic mass size of 32 mm or larger (p = 0.009), lesion size of 30 mm on MR (p = 0.004), lower signal intensity (SI) on fat-saturated T2-weighted MR images (FS-T2WI) (p = 0.005), heterogeneous or rim enhancement on MR images (p = 0.009), and apparent diffusion coefficient (ADC) values lower than 1.04 × 10−3 mm2/s on diffusion-weighted MR imaging (DWI) (p < 0.001). Conclusion Clinical symptom of palpability or nipple discharge, number of core specimen, mammographic maximum lesion or mass size, SI on FS-T2WI, heterogeneous or rim enhancement on MR, and ADC value may be helpful in predicting the upgrade to invasive breast cancer for DCIS diagnosed at US-guided 14-gauge CNB.
    European journal of radiology 04/2014; 83(4). DOI:10.1016/j.ejrad.2014.01.010 · 2.16 Impact Factor
  • European Journal of Radiology 09/2012; 81:S189–S191. DOI:10.1016/S0720-048X(12)70078-5 · 2.16 Impact Factor
  • [Show abstract] [Hide abstract]
    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