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.
OBJECTIVE
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.


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.
Pediconi
Federica
F
Department of Radiological Sciences, University of Rome La Sapienza, Rome, Italy. federica.pediconi@uniroma1.it
Catalano
Carlo
C
Venditti
Fiammetta
F
Ercolani
Mauro
M
Carotenuto
Luigi
L
Padula
Simona
S
Moriconi
Enrica
E
Roselli
Antonella
A
Giacomelli
Laura
L
Kirchin
Miles A
MA
Passariello
Roberto
R
D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D003116:Color; D003287:Contrast Media; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D008327:Mammography; D008875:Middle Aged; D012815:Signal Processing, Computer-Assisted; D012984:Software
D003287:Contrast Media