Magnetic resonance imaging contrast-enhanced relaxometry of breast tumors: an MRI multicenter investigation concerning 100 patients.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using extracellular contrast agents has proved to be useful for the characterization of breast tumors. DCE-MRI has demonstrated a high sensitivity (around 95%) but a rather poor and controversial specificity, varying, according to the different studies, from 45% to 90%. In order to increase (a) the specificity and (b) the robustness of this quantitative approach in multicenter evaluation (five MRI units), a quantitative approach called dynamic relaxometry has been developed. According to the proposed method, the time-dependent longitudinal relaxation rate measured on region of interest of the lesion was calculated during the contrast uptake, after intravenous bolus injection of contrast agent. A specifically developed method was used for fast R(1) measurements. Relaxometry time curves are fitted to the Tofts model allowing the measurement of the parameters describing the enhancement curve (maximum relation rate enhancement, initial, 30-s and 60-s slopes) and the tissue parameters [transfer constant (K(trans) min(-1)) and extracellular extravascular space fraction (v(e))]. Correspondence factorial analysis followed by hierarchical ascendant classification are then performed on the different parameters. Higher K(trans) values were observed in infiltrative ductal carcinomas than in infiltrative lobular carcinomas, in agreement with data published by other groups. Specificity of DCE-MRI has been increased up to 85%, with a sensitivity of 95% with K(trans)/v(e) and enhancement index I (ratio of initial slope by maximum relaxation rate enhancement). A multiparametric data analysis of the calculated parameters opens the way to include quantitative image-based information in new nosologic approaches to breast tumors.
Eliat
Pierre-Antoine
PA
Laboratoire de Résonance Magnétique en Biologie et Médecine, IFR91 Imagerie Métabolique et Modélisation, Faculté de Médecine, Université de Rennes 1 CS 34317-35043 Rennes Cedex, France. pierre-antoine.eliat@univ-rennes1.fr
Dedieu
Véronique
V
Bertino
Catherine
C
Bouté
Véronique
V
Lacroix
Joëlle
J
Constans
Jean-Marc
JM
de Korvin
Brigitte
B
Vincent
Catherine
C
Bailly
Corinne
C
Joffre
Francis
F
de Certaines
Jacques
J
Vincensini
Dominique
D
D002288:Adenocarcinoma, Mucinous / Q000175:diagnosis; D000328:Adult; D000368:Aged; D001943:Breast Neoplasms / Q000175:diagnosis*; D018270:Carcinoma, Ductal, Breast / Q000175:diagnosis; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000175:diagnosis; D018275:Carcinoma, Lobular / Q000175:diagnosis; D003287:Contrast Media; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity
D003287:Contrast Media