Literature DB >> 15973137

Color-coded automated signal intensity curves for detection and characterization of breast lesions: preliminary evaluation of a new software package for integrated magnetic resonance-based breast imaging.

Federica Pediconi1, Carlo Catalano, Fiammetta Venditti, Mauro Ercolani, Luigi Carotenuto, Simona Padula, Enrica Moriconi, Antonella Roselli, Laura Giacomelli, Miles A Kirchin, Roberto Passariello.   

Abstract

OBJECTIVES: The objective of this study was to evaluate the value of a color-coded automated signal intensity curve software package for contrast-enhanced magnetic resonance mammography (CE-MRM) in patients with suspected breast cancer.
MATERIALS AND METHODS: Thirty-six women with suspected breast cancer based on mammographic and sonographic examinations were preoperatively evaluated on CE-MRM. CE-MRM was performed on a 1.5-T magnet using a 2D Flash dynamic T1-weighted sequence. A dosage of 0.1 mmol/kg of Gd-BOPTA was administered at a flow rate of 2 mL/s followed by 10 mL of saline. Images were analyzed with the new software package and separately with a standard display method. Statistical comparison was performed of the confidence for lesion detection and characterization with the 2 methods and of the diagnostic accuracy for characterization compared with histopathologic findings.
RESULTS: At pathology, 54 malignant lesions and 14 benign lesions were evaluated. All 68 (100%) lesions were detected with both methods and good correlation with histopathologic specimens was obtained. Confidence for both detection and characterization was significantly (P < or = 0.025) better with the color-coded method, although no difference (P > 0.05) between the methods was noted in terms of the sensitivity, specificity, and overall accuracy for lesion characterization. Excellent agreement between the 2 methods was noted for both the determination of lesion size (kappa = 0.77) and determination of SI/T curves (kappa = 0.85).
CONCLUSIONS: The novel color-coded signal intensity curve software allows lesions to be visualized as false color maps that correspond to conventional signal intensity time curves. Detection and characterization of breast lesions with this method is quick and easily interpretable.

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Year:  2005        PMID: 15973137     DOI: 10.1097/01.rli.0000167427.33581.f3

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  6 in total

1.  Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis.

Authors:  A Karahaliou; K Vassiou; N S Arikidis; S Skiadopoulos; T Kanavou; L Costaridou
Journal:  Br J Radiol       Date:  2010-04       Impact factor: 3.039

2.  Classification of small contrast enhancing breast lesions in dynamic magnetic resonance imaging using a combination of morphological criteria and dynamic analysis based on unsupervised vector-quantization.

Authors:  Thomas Schlossbauer; Gerda Leinsinger; Axel Wismuller; Oliver Lange; Michael Scherr; Anke Meyer-Baese; Maximilian Reiser
Journal:  Invest Radiol       Date:  2008-01       Impact factor: 6.016

3.  3-T dynamic contrast-enhanced MRI of the breast: pharmacokinetic parameters versus conventional kinetic curve analysis.

Authors:  Riham H El Khouli; Katarzyna J Macura; Ihab R Kamel; Michael A Jacobs; David A Bluemke
Journal:  AJR Am J Roentgenol       Date:  2011-12       Impact factor: 3.959

4.  Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score.

Authors:  Anja Baltzer; Matthias Dietzel; Clemens G Kaiser; Pascal A Baltzer
Journal:  Eur Radiol       Date:  2015-06-27       Impact factor: 5.315

5.  Case of the season: a giant fibroadenoma in the guise of a phyllodes tumor; characterization role of MRI.

Authors:  Riham H El Khouli; Adeline Louie
Journal:  Semin Roentgenol       Date:  2009-04       Impact factor: 0.800

6.  Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer.

Authors:  Matthias Dietzel; Rüdiger Schulz-Wendtland; Stephan Ellmann; Ramy Zoubi; Evelyn Wenkel; Matthias Hammon; Paola Clauser; Michael Uder; Ingo B Runnebaum; Pascal A T Baltzer
Journal:  Sci Rep       Date:  2020-02-28       Impact factor: 4.379

  6 in total

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