Rationale and Objectives
To compare the performance of computer aided detection (CAD) systems on pairs of full-field digital mammogram (FFDM) and screen-film mammogram (SFM) obtained from the same patients.
Materials and Methods
Our CAD systems on both modalities have similar architectures that consist of five steps. For FFDMs, the input raw image is first log-transformed and enhanced by a multiresolution preprocessing scheme. For digitized SFMs, the input image is smoothed and subsampled to a pixel size of 100 μm × 100 μm. For both CAD systems, the mammogram after preprocessing undergoes a gradient field analysis followed by clustering-based region growing to identify suspicious breast structures. Each of these structures is refined in a local segmentation process. Morphologic and texture features are then extracted from each detected structure, and trained rule-based and linear discriminant analysis classifiers are used to differentiate masses from normal tissues. Two datasets, one with masses and the other without masses, were collected. The mass dataset contained 131 cases with 131 biopsy proven masses, of which 27 were malignant and 104 benign. The true locations of the masses were identified by an experienced Mammography Quality Standards Act (MQSA) radiologist. The no-mass data set contained 98 cases. The time interval between the FFDM and the corresponding SFM was 0 to 118 days.
Results
Our CAD system achieved case-based sensitivities of 70%, 80%, and 90% at 0.9, 1.5, and 2.6 false positive (FP) marks/image, respectively, on FFDMs, and the same sensitivities at 1.0, 1.4, and 2.6 FP marks/image, respectively, on SFMs.
Conclusions
The difference in the performances of our FFDM and SFM CAD systems did not achieve statistical significance.
Full-field digital mammography (FFDM) and screen-film mammography (SFM) are two available methods for breast cancer screening in clinical practice. FFDM detectors provide higher detective quantum efficiency (DQE) and signal-to-noise ratio (SNR), wider dynamic range, and higher contrast sensitivity than SFM. FFDM may alleviate some of the limitations of SFM, especially in breasts with dense fibroglandular tissue ( ). In the last few years, several FFDM systems became commercially available because of the potential of digital imaging to improve breast cancer detection.
Several clinical trials have been conducted to compare radiologists’ interpretation on FFDMs and SFMs. Lewin et al ( ) conducted a clinical study to compare FFDMs and SFMs for the detection of breast cancer in 6,737 examinations of women 40 years of age and older collected from two institutions. Forty-two cancers were detected within this population. The difference in cancer detection was not statistically significant ( P > .1) between FFDMs and SFMs. FFDMs resulted in fewer recalls than did SFM, which was statistically significant ( P < .001). Another clinical trial ( ) aiming at collecting data for US Food and Drug Administration approval included SFMs and FFDMs of 676 women who were scheduled to undergo breast biopsy. The average area under the receiver operating characteristic (ROC) curve, the sensitivity and the specificity were 0.715, 0.66 and 0.67 for printed FFDM and 0.765, 0.74, 0.60 for SFM, respectively. However, none of these differences achieved statistical significance. Skaane et al ( ) has conducted several clinical studies to compare SFM and FFDM with soft-copy interpretation for reader performance in detection and classification of breast lesions. According to their findings, there was no significant difference between FFDM and SFM either in detection or in classification. A recent study by Pisano et al ( ) collected a total of 49,528 patients at 33 sites in the United States and Canada. Mammograms were interpreted independently by two radiologists. The overall diagnostic accuracy of FFDMs and SFMs for breast cancers was similar. However, FFDM was more accurate in women younger than age 50 years, women with radiographically dense breasts, and premenopausal or perimenopausal women.
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Materials and methods
Materials
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Table 1
Description of Cases in the Mass Datasets and Subsets for Training and Testing in the Twofold Cross-Validation Scheme
Mass Set Mass Subset 1 Mass Subset 2 FFDM SFM FFDM SFM FFDM SFM Total number of cases 131 131 65 65 66 66 Total number of images 262 262 130 130 132 132 Number of visible masses (by case) 131 130 65 65 66 65 Number of masses only visible on one view 8 9 5 5 3 4 Number of visible masses (by image) 254 251 125 125 129 126 Number of visible malignant masses 27 27 12 12 15 15 Number of visible benign masses 104 103 53 53 51 50
FFDM: full-field digital mammogram; SFM: screen-film mammogram.
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Methods
CAD System
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Training and Test CAD System
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Evaluation Methods
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Results
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Table 2
Estimation of the Statistical Significance of the Difference in the FROC Performances Between the FFDM and SFM CAD Systems
A 1 (AFROC) FOM (JAFROC) All Cases Malignant Cases All Cases Malignant Cases Test Subset 1 Test Subset 2 Test Subset 1 Test Subset 2 Test Subset 1 Test Subset 2 Test Subset 1 Test Subset 2 FFDM 0.48 0.49 0.51 0.49 0.47 0.48 0.55 0.47 SFM 0.42 0.43 0.47 0.42 0.46 0.41 0.48 0.42P values .17 .16 .56 .23 .73 .33 .29 .59
The FROC curves with the FP marker rates obtained from the no-mass dataset were compared.
FROC, ; FFDM, full-field digital mammogram; SFM, screen-film mammogram; CAD, computed-aided detection; AFROC, alternative free-response receiver operating characteristic; FOM, figure-of-merit; JAFROC, jackknife free-response ROC.
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Discussion
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Conclusion
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