Original ArticleColor-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 ImagingPediconi, Federica MD*; Catalano, Carlo MD*; Venditti, Fiammetta MD*; Ercolani, Mauro Eng†; Carotenuto, Luigi Eng†; Padula, Simona MD*; Moriconi, Enrica MD*; Roselli, Antonella MD*; Giacomelli, Laura MD‡; Kirchin, Miles A. PhD§; Passariello, Roberto MD*Author Information From the Departments of *Radiological Sciences and ‡Surgery, University of Rome “La Sapienza,” Rome, Italy; †Tecnobiomedica S.p.A., Pomezia, Italy; and §Worldwide Medical Affairs, Bracco Imaging SpA, Milano, Italy. Received December 20, 2004 and accepted for publication, after revision, March 20, 2004. Reprints: Federica Pediconi, MD, Department of Radiological Sciences, University of Rome “La Sapienza,” V.le Regina Elena, 324, 00161 Rome, Italy. E-mail: [email protected]. Investigative Radiology: July 2005 - Volume 40 - Issue 7 - p 448-457 doi: 10.1097/01.rli.0000167427.33581.f3 Buy Metrics 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 ≤ 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. © 2005 Lippincott Williams & Wilkins, Inc.