Discrimination between breast invasive ductal carcinomas and benign lesions by optimizing quantitative parameters derived from dynamic contrast-enhanced MRI using a semi-automatic method

Background

To propose a semi-automatic method for distinguishing invasive ductal carcinomas from benign lesions on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

142 cases were included. In the conventional method, the region of interest for a breast lesion was drawn manually and the corresponding mean time–signal intensity curve (TIC) was qualitatively categorized. Only one quantitative parameter was obtained: the maximum slope of increase (MSI). By contrast, the proposed method extracted the suspicious breast lesion semi-automatically. Besides MSI, more quantitative parameters reflecting perfusion information were derived from the mean TIC and lesion region, including the signal intensity slope (SI_slope), initial percentage of enhancement, percentage of peak enhancement, early signal enhancement ratio, and second enhancement percentage. The mean TIC was categorized quantitatively according to the value of SI_slope. Regression models were established. The diagnostic performance differed between the new and conventional methods according to the Wilcoxon rank-sum test and receiver operating characteristic analysis.

Results

According to the TIC categorization results, the accuracies of the traditional and the new method were 59.16% and 76.05%, respectively (P < 0.05). The accuracy was 63.35% for MSI, which was derived from the manual method. For the semi-automatic method, the accuracies were 81.0% and 78.9% for the lesion region and the corresponding mean TIC regression models, respectively.

Conclusions

The results demonstrate that our proposed semi-automatic method is beneficial for discriminating breast IDCs and benign lesions based on DCE-MRI, and this method should be considered as a supplementary tool for subjective diagnosis by clinical radiologists.