Rationale and Objectives
To convert and optimize our previously developed computerized analysis methods for use with images from full-field digital mammography (FFDM) for breast mass classification to aid in the diagnosis of breast cancer.
Materials and Methods
An institutional review board approved protocol was obtained, with waiver of consent for retrospective use of mammograms and pathology data. Seven hundred thirty-nine FFDM images, which contained 287 biopsy-proven breast mass lesions, of which 148 lesions were malignant and 139 lesions were benign, were retrospectively collected. Lesion margins were delineated by an expert breast radiologist and were used as the truth for lesion-segmentation evaluation. Our computerized image analysis method consisted of several steps: 1) identified lesions were automatically extracted from the parenchymal background using computerized segmentation methods; 2) a set of image characteristics (mathematic descriptors) were automatically extracted from image data of the lesions and surrounding tissues; and 3) selected features were merged into an estimate of the probability of malignancy using a Bayesian artificial neural network classifier. Performance of the analyses was evaluated at various stages of the conversion using receiver-operating characteristic analysis.
Results
An area under the curve value of 0.81 was obtained in the task of distinguishing between malignant and benign mass lesions in a round-robin by case evaluation on the entire FFDM dataset. We failed to show a statistically significant difference ( P = .83) compared to results from our previous study in which the computerized classification was performed on digitized screen-film mammograms.
Conclusions
Our computerized analysis methods developed on digitized screen-film mammography can be converted for use with FFDM. Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.
Breast cancer is the most frequently diagnosed cancer in women in the United States ( ). An estimated 178,480 new cases of invasive breast cancer and 62,030 new cases of in situ breast cancer are expected to occur among women during 2007. An estimated 40,460 breast cancer deaths are expected in 2007 ( ). Screening mammography has been the most effective tool for early cancer detection over the past several decades ( ), and it has been shown to reduce the cancer mortality by as much as 40% ( ). In addition, computer-aided detection methods have been shown to improve the detection of more cancers in mammography screening ( ).
After a lesion is detected, diagnostic imaging workup is performed to determine if a biopsy is warranted. Computer-aided diagnosis (CADx) has been proposed to aid the radiologist during diagnostic mammographic interpretation ( ). Most of the computerized analysis methods have been developed using databases of digitized screen-film mammograms (SFM D ) ( ). In recent years, full-field digital mammography (FFDM) has been approved by the Food and Drug Administration for clinical use. There were 13,559 mammography units, in which 13.8% were FFDM units, as of October 1, 2006, in the United States ( ). Because of the digital nature of FFDM, it offers many advantages, such as image storage, image transmission and retrieval, and digital image processing. With the easy access to digital images, computerized image analyses can be directly applied to FFDM without the need for film digitization, as is needed with screen-film mammography.
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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
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Table 1
Performances for the Computed-aided Diagnosis Method at Various Stages of Conversion in the Task of Distinguishing between Malignant and Benign Lesions
Classifiers Training Cases Feature Selection Classifier Weights Testing Cases Testing Methods AUC ± SE 95% CI Evaluation #1 SFM D SFM D SFM D FFDM Independent 0.74 ± 0.03 [0.69, 0.80] Evaluation #2 FFDM SFM D FFDM FFDM Round-robin by case 0.77 ± 0.03 [0.72, 0.82] Evaluation #3 FFDM FFDM (15 features) FFDM FFDM Round-robin by case 0.78 ± 0.03 [0.73, 0.83] Evaluation #4 FFDM FFDM (29 features) FFDM FFDM Round-robin by case 0.81 ± 0.03 [0.76, 0.86] Prior evaluation for comparison (20) SFM D SFM D SFM D SFM D Independent 0.81 ± 0.05 [0.69, 0.88]
AUC: area under the curve; FFDM: full-field digital mammography; SE: standard error; SFM D : digitized screen-film mammograms.
Here, “by case” round-robin analysis was performed on the FFDM dataset ( n = 287 breast mass lesions).
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Table 2
Statistical Analysis Results for Differences in the Performance among Different Neural Network Classifiers that were used in SFM D and FFDM Studies
Evaluation #1 (FFDM) Evaluation #2 (FFDM) Evaluation #3 (FFDM) Evaluation #4 (FFDM) Prior Evaluation (20) (SFM D ) Evaluation #1 (FFDM) — 0.20 0.17 0.016 (α = 0.005) 0.32 Evaluation #2 (FFDM) — — 0.70 0.09 0.66 Evaluation #3 (FFDM) — — — 0.16 0.77 Evaluation #4 (FFDM) — — — — 0.83 Prior evaluation⁎ (SFM D ) — — — — —
FFDM: full-field digital mammography; SFM D : digitized screen-film mammograms.
From ROCKIT, P values were calculated for differences in the classification performance for a pair of neural network classifiers. The significance level α for the individual test was calculated using Holm’s procedure (overall α T = 0.05) for multiple tests of significance ( ).
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Discussion
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