Rationale and Objectives
To investigate a multimodality computer-aided diagnosis (CAD) scheme that combines image information from full-field digital mammography (FFDM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for computerized breast cancer classification.
Materials and Methods
From a retrospective FFDM database with 432 lesions (255 malignant, 177 benign) and a retrospective DCE-MRI database including 476 lesions (347 malignant, 129 benign), we constructed a multimodality dataset of 213 lesions (168 malignant, 45 benign). Each lesion was present on both FFDM and DCE-MRI images and deemed to be a difficult case given the necessity of having both clinical imaging exams. Using a manually indicated lesion location (ie, a seed point on FFDM images or a region of interest on DCE-MRI images, the computer automatically segmented the mass lesions and extracted lesion features). A subset of features was selected using linear stepwise feature selection and merged by a Bayesian artificial neural network to yield an estimate of the probability of malignancy. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the selected features in distinguishing between malignant and benign lesions.
Results
With leave-one-lesion-out cross-validation on the multimodality dataset, the mammography-only features yielded an area under the ROC curve (AUC) of 0.74 ± 0.04, and the DCE-MRI-only features yielded an AUC of 0.78 ± 0.04. The combination of these two modalities, which included a spiculation feature from mammography and two kinetic features from DCE-MRI, yielded an AUC of 0.87 ± 0.03. The improvement of combining multimodality information was statistically significant as compared to the use of single modality information alone.
Conclusions
A CAD scheme that combines features extracted from FFDM and DCE-MRI images may be advantageous to single-modality CAD in the task of differentiating between malignant and benign lesions.
Breast cancer is a leading cause of mortality in American women, with an estimated 192,370 new cancer cases and 40,170 deaths in the United States in 2009 . Although there are limited methods for curing breast cancer, recent statistics show that there has been a steady decrease in the annual death rate from breast cancer among women, from 32.69 in 1991 to 24.00 in 2005 (per 100,000 population). This decrease accounts for nearly 40% of decreases in cancer death rates for women and largely reflects improvements in early detection and treatment .
Medical imaging plays a crucial role in reducing breast cancer mortality, with contributions to early detection through screening, diagnosis, image-guided biopsy, treatment planning, and treatment response monitoring . As the primary imaging modality for early detection and diagnosis of breast cancer, mammography has achieved significant success and has reduced the mortality from breast cancer by 15%–35% . However, about 15%–20% of cancers are still missed, and 65%–85% of breast biopsies are performed on benign lesions . Consequently, complementary imaging modalities, such as breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and breast sonography (ie, breast ultrasound imaging), are being investigated to improve the accuracy of breast cancer diagnosis.
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Materials and methods
Database
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Computerized Classification Methods
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Performance Evaluation and Statistical Analysis
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Results
Classification Performance of Single-Modality CAD
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Classification Performance of Single-modality CAD on the Multimodality Subset
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Classification performance of multimodality CAD
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Table 1
Classification Performance of the Multimodality CAD Method in a Leave-one-lesion-out Cross-validation for the Task of Differentiating Malignant and Benign Lesions, and the Comparison with the Performance of Single-modality CAD
Modality_AUC M_ ± SE_AUC S_ ± SE_P_ Value Significance Level 95% CI of ▵ AUC FFDM and DCE-MRI 0.87 ± 0.03 — — — — FFDM (scenario 1) — 0.71 ± 0.04 <10 −4 0.0125 [0.09, 0.23] FFDM (scenario 2) — 0.74 ± 0.04 .002 0.0250 [0.05, 0.20] DCE-MRI (scenario 1) — 0.77 ± 0.04 .009 0.0500 [0.03, 0.15] DCE-MRI (scenario 2) — 0.78 ± 0.04 .004 0.0375 [0.02, 0.13]
CAD, computer-aided diagnosis; FFDM, full-field digital mammography; DCE-MRI, dynamic contrast-enhanced magnetic resonance imaging.
The value after “±” is the standard error (SE) associated with each AUC . The two-tailed P value and 95% CI of ▵ AUC were calculated by ROCKIT. The significance level column represents the significance level of individual tests adjusted with Holm t -test (overall significance level α T = 0.05).
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Discussion
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Conclusion
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Acknowledgment
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