A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution

Authors

  • Dalmış Mehmet Ufuk,

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author
  • Gubern-Mérida Albert,

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author
  • Vreemann Suzan,

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author
  • Karssemeijer Nico,

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author
  • Mann Ritse,

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author
  • Platel Bram

    1. Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, Route 766 Nijmegen, Gelderland 6500 HB, The Netherlands
    Search for more papers by this author

Abstract

Purpose:

With novel MRI sequences, high spatiotemporal resolution has become available in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Since benign structures in the breast can show enhancement similar to malignancies in DCE-MRI, characterization of detected lesions is an important problem. The purpose of this study is to develop a computer-aided diagnosis (CADx) system for characterization of breast lesions imaged with high spatiotemporal resolution DCE-MRI.

Methods:

The developed CADx system is composed of four main parts: semiautomated lesion segmentation, automated computation of morphological and dynamic features, aorta detection, and classification between benign and malignant categories. Lesion segmentation is performed by using a “multiseed smart opening” algorithm. Five morphological features were computed based on the segmentation of the lesion. For each voxel, contrast enhancement curve was fitted to an exponential model and dynamic features were computed based on this fitted curve. Average and standard deviations of the dynamic features were computed over the entire segmented area, in addition to the average value in an automatically selected smaller “most suspicious region.” To compute the dynamic features for an enhancement curve, information of aortic enhancement is also needed. To keep the system fully automated, the authors developed a component which automatically detects the aorta and computes the aortic enhancement time. The authors used random forests algorithm to classify benign lesions from malignant. The authors evaluated this system in a dataset of breast MRI scans of 325 patients with 223 malignant and 172 benign lesions and compared its performance to an existing approach. The authors also evaluated the classification performances for ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) lesions separately. The classification performances were measured by receiver operating characteristic (ROC) analysis in a leave-one-out cross validation scheme.

Results:

The area under the ROC curve (AUC) obtained by the proposed CADx system was 0.8543, which was significantly higher (p = 0.007) than the performance obtained by the previous CADx system (0.8172) on the same dataset. The AUC values for DCIS, IDC, and ILC lesions were 0.7924, 0.8688, and 0.8650, respectively.

Conclusions:

The authors developed a CADx system for high spatiotemporal resolution DCE-MRI of the breast. This system outperforms a previously proposed system in classifying benign and malignant lesions, while it requires less user interactions.

Ancillary