Can semi-quantitative evaluation of uncertain (type II) time-intensity curves improve diagnosis in breast DCE-MRI?

Roberta Fusco, Salvatore Filice, Vincenza Granata, Ylenia Mandato, Annamaria Porto, Massimiliano D’Aiuto, Massimo Rinaldo, Maurizio Di Bonito, Mario Sansone, Carlo Sansone, Antonio Rotondo, Antonella Petrillo Petrillo

Department of Breast Surgery and Oncology, National Cancer Institute of Naples “Pascale Foundation”, Naples, Italy.
Department of Diagnostic and Laboratory Pathology, National Cancer Institute of Naples “Pascale Foundation”, Naples, Italy.
Department of Diagnostic Imaging, National Cancer Institute of Naples “Pascale Foundation”, Naples, Italy.
Department of Diagnostic Imaging, Second University of Naples (SUN), Naples, Italy.
Department of Electrical Engineering and Information Technology, University “Federico II” of Naples, Naples, Italy.

DOI: 10.4236/jbise.2013.63A052                

Abstract

Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)- averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histological-cytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.

Keywords

Breast Cancer; Dynamic Contrast Enhanced-MRI; Time Intensity Curve; Tracer Kinetics; Semi-Quantitative Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

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