Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers

Abstract

Purpose: To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions.

Materials and methods: In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis.

Results: Differentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 +/- 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions.

Conclusion: Computer-aided diagnosis of breast DCE MR imaging-depicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.

Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09090838/-/DC1.

Figure 1:

Graph illustrates distribution of lesion volumes for all IDC lesions with positive LNs, IDC lesions with negative LNs, DCIS lesions, and benign lesions in the breast MR imaging database.

Figure 2:

Diagram outlines the protocol for automated analysis of breast lesions seen at DCE MR imaging. BANN = Bayesian artificial neural network, FCM = fuzzy c-means clustering, LDA = linear discriminant analysis.

Figure 3a:

Coronal MR images show segmentation (red outline) of (a) IDC lesion with positive LNs in 34-year-old woman, (b) IDC lesion with negative LNs in 39-year-old woman, (c) DCIS lesion in 66-year-old woman, and (d) benign lesion in 48-year-old woman. (e) Corresponding characteristic kinetic curves for these four breast lesions.

Figure 3b:

Coronal MR images show segmentation (red outline) of (a) IDC lesion with positive LNs in 34-year-old woman, (b) IDC lesion with negative LNs in 39-year-old woman, (c) DCIS lesion in 66-year-old woman, and (d) benign lesion in 48-year-old woman. (e) Corresponding characteristic kinetic curves for these four breast lesions.

Figure 3c:

Coronal MR images show segmentation (red outline) of (a) IDC lesion with positive LNs in 34-year-old woman, (b) IDC lesion with negative LNs in 39-year-old woman, (c) DCIS lesion in 66-year-old woman, and (d) benign lesion in 48-year-old woman. (e) Corresponding characteristic kinetic curves for these four breast lesions.

Figure 3d:

Coronal MR images show segmentation (red outline) of (a) IDC lesion with positive LNs in 34-year-old woman, (b) IDC lesion with negative LNs in 39-year-old woman, (c) DCIS lesion in 66-year-old woman, and (d) benign lesion in 48-year-old woman. (e) Corresponding characteristic kinetic curves for these four breast lesions.

Figure 3e:

Coronal MR images show segmentation (red outline) of (a) IDC lesion with positive LNs in 34-year-old woman, (b) IDC lesion with negative LNs in 39-year-old woman, (c) DCIS lesion in 66-year-old woman, and (d) benign lesion in 48-year-old woman. (e) Corresponding characteristic kinetic curves for these four breast lesions.

Figure 4a:

Graphs show (a) relationships between homogeneity and circularity for IDC lesions with positive LNs, IDC lesions with negative LNs, DCIS lesions, and benign lesions and (b) relationships between homogeneity and circularity for IDC lesions with positive lymph nodes, IDC lesions with negative lymph nodes, and DCIS lesions.

Figure 4b:

Graphs show (a) relationships between homogeneity and circularity for IDC lesions with positive LNs, IDC lesions with negative LNs, DCIS lesions, and benign lesions and (b) relationships between homogeneity and circularity for IDC lesions with positive lymph nodes, IDC lesions with negative lymph nodes, and DCIS lesions.

Figure 5a:

Receiver operating characteristic curves for (a) invasive cancer classification tasks and (b) LN metastasis classification tasks.

Figure 5b:

Receiver operating characteristic curves for (a) invasive cancer classification tasks and (b) LN metastasis classification tasks.