Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Mar;254(3):680-90.
doi: 10.1148/radiol.09090838. Epub 2010 Feb 1.

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

Neha Bhooshan  1 , Maryellen L Giger, Sanaz A Jansen, Hui Li, Li Lan, Gillian M Newstead
Affiliations

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

Neha Bhooshan et al. Radiology. 2010 Mar.

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.

PubMed Disclaimer

Figures

Figure 1:
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:
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:
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:
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:
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:
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:
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:
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:
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:
Figure 5a:
Receiver operating characteristic curves for (a) invasive cancer classification tasks and (b) LN metastasis classification tasks.
Figure 5b:
Figure 5b:
Receiver operating characteristic curves for (a) invasive cancer classification tasks and (b) LN metastasis classification tasks.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Schnall MD. Breast MR imaging. Radiol Clin North Am 2003;41:43–50 - PubMed
    1. Morris EA. Breast cancer imaging with MRI. Radiol Clin North Am 2002;40:443–466 - PubMed
    1. Kuhl CK, Schild HH. Dynamic image interpretation of MRI of the breast. J Magn Reson Imaging 2000;12:965–974 - PubMed
    1. Kuhl CK, Mielcareck P, Klaschik S, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions?. Radiology 1999;211:101–110 - PubMed
    1. Bartella L, Smith CS, Dershaw DD, Liberman L. Imaging breast cancer. Radiol Clin North Am 2007;45:45–67 - PubMed

Publication types