Computer-aided detection in direct digital full-field mammography: initial results

For the first time, full-field digital mammography (FFDM) allows computer-aided detection (CAD) analysis of directly acquired digital image data. The purpose of this study was to evaluate a CAD system in patients with histologically correlated breast cancer depicted with FFDM. Sixty-three cases of histologically proven breast cancer detected with FFDM (Senographe 2000D, GE Medical Systems, Buc, France) were analyzed using a CAD system (Image Checker V2.3, R2 Technology, Los Altos, Calif.). Fourteen of these malignancies were characterized as microcalcifications, 37 as masses, and 12 as both. The mammographic findings were categorized as BI-RADS 3 (n=5), BI-RADS 4 (n=17) and BI-RADS 5 (n=40). The sensitivity for malignant lesions and the rate of false-positive marks per image were calculated. The sensitivity and its 95% confidence interval (CI) were estimated. The sensitivity of the CAD R2 system in breast cancer seen on FFDM was 89% for microcalcifications [CI_95%=(70%; 98%)] and 81% for masses [CI_95%=(67%; 91%)]. As expected, the detection rate was higher in lesions categorized as BI-RADS 5 (37 of 40) compared with lesions categorized as BI-RADS 4 (11 of 17). In the group categorized as BI-RADS 3 the detection rate was 4 of 5 lesions; however, this group was very small. The rate of false-positive marks was 0.35 microcalcification marks/image and 0.26 mass marks/image. The overall rate of false-positive marks was 0.61 per image. CAD based on FFDM provides an optimized work flow. Results are equivalent to the results reported for CAD analysis of secondarily digitized image data. Sensitivity for microcalcifications is acceptable and for masses is low. The number of false-positive marks per image should be reduced. Electronic Publication     

Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion

OBJECTIVE: Computer-aided detection (CAD) algorithms have successfully revealed breast masses and microcalcifications on screening mammography. The purpose of our study was to evaluate the sensitivity of commercially available CAD systems for revealing architectural distortion, the third most common appearance of breast cancer. MATERIALS AND METHODS: Two commercially available CAD systems were used to evaluate screening mammograms obtained in 43 patients with 45 mammographically detected regions of architectural distortion. For each CAD system, we determined the sensitivity for revealing architectural distortion on at least one image of the two-view mammographic examination (case sensitivity) and for each individual mammogram (image sensitivity). Surgical biopsy results were available for each case of architectural distortion. RESULTS: Architectural distortion was deemed present and actionable by a panel of expert breast imagers in 80 views of the 45 cases. One CAD system detected distortion in 22 of 45 cases of distortion (case sensitivity, 49%) and in 30 of 80 mammograms (image sensitivity, 38%); it displayed 0.7 false-positive marks per image. Another CAD system identified distortion in 15 of 45 cases (case sensitivity, 33%) and 17 of 80 mammograms (image sensitivity, 21%); it displayed 1.27 false-positive marks per image. Sensitivity for malignancy-caused distortion was similar to or lower than sensitivity for all causes of distortion. CONCLUSION: Fewer than one half of the cases of architectural distortion were detected by the two most widely available CAD systems used for interpretations of screening mammograms. Considerable improvement in the sensitivity of CAD systems is needed for detecting this type of lesion. Practicing breast imagers who use CAD systems should remain vigilant for architectural distortion.     

Clinical performance of computer-assisted detection (CAD system in detecting carcinoma in breasts of different densities

OBJECTIVES: To determine the clinical performance of a computer-assisted detection (CAD) system in detecting carcinoma in breasts of different densities. MATERIALS AND METHODS: A total of 264 sets of bilateral screening mammograms taken in craniocaudal and medial-lateral oblique projections during the year 1997 were divided into four groups according to the BI-RADS density classification: fatty (pattern 1), scattered fibroglandular (pattern 2), heterogeneously dense (pattern 3) and extremely dense (pattern 4). Each group contained about 60% normal and 40% biopsy-proven cancer cases. Of the malignant cases, there were a mixture of mammographic findings including focal masses (<2.5 cm), asymmetrical density, architectural distortion or microcalcifications. Films with artefacts and obvious masses>2.5 cm were not included. The chosen cases were then digitized and analysed by the CAD system. Sensitivity was calculated as detection of cancer by at least one marker in at least one view. Specificity was calculated as the number of false-positive marks per image on normal cases. Statistical tests of significance were performed by using contingency tables and Chi square test. RESULTS: The CAD system detected 14 out of the total 15 cancer cases in totally fatty breasts with a sensitivity of 93.3% at a specificity of 1.3 false-positive marks per image. In breasts with scattered fibroglandular pattern, the sensitivity was 93.9% (31/33) and the specificity was 1.6 false-positive marks per image while in heterogeneously dense breasts, the sensitivity of the CAD system fell to 84.8% at a specificity of 1.6 false-positive marks per image. The sensitivity of the CAD system further dropped to 64.3% in markedly dense breasts while maintaining a specificity of 1.2 false-positive marks per image. The decrease in sensitivity in dense breast was found to be significant (p=0.046). CONCLUSION: The sensitivity of the CAD system deteriorated significantly as the density of the breast increased while the specificity of the system remained relatively constant.     

Impact of breast density on computer-aided detection for breast cancer

OBJECTIVE: Our aim was to determine whether breast density affects the performance of a computer-aided detection (CAD) system for the detection of breast cancer. MATERIALS AND METHODS: Nine hundred six sequential mammographically detected breast cancers and 147 normal screening mammograms from 18 facilities were classified by mammographic density. BI-RADS 1 and 2 density cases were classified as nondense breasts; BI-RADS 3 and 4 density cases were classified as dense breasts. Cancers were classified as either masses or microcalcifications. All mammograms from the cancer and normal cases were evaluated by the CAD system. The sensitivity and false-positive rates from CAD in dense and nondense breasts were evaluated and compared. RESULTS: Overall, 809 (89%) of 906 cancer cases were detected by CAD; 455/505 (90%) cancers in nondense breasts and 354/401 (88%) cancers in dense breasts were detected. CAD sensitivity was not affected by breast density (p=0.38). Across both breast density categories, 280/296 (95%) microcalcification cases and 529/610 (87%) mass cases were detected. One hundred fourteen (93%) of the 122 microcalcifications in nondense breasts and 166 (95%) of 174 microcalcifications in dense breasts were detected, showing that CAD sensitivity to microcalcifications is not dependent on breast density (p=0.46). Three hundred forty-one (89%) of 383 masses in nondense breasts, and 188 (83%) of 227 masses in dense breasts were detected-that is, CAD sensitivity to masses is affected by breast density (p=0.03). There were more false-positive marks on dense versus nondense mammograms (p=0.04). CONCLUSION: Breast density does not impact overall CAD detection of breast cancer. There is no statistically significant difference in breast cancer detection in dense and nondense breasts. However, the detection of breast cancer manifesting as masses is impacted by breast density. The false-positive rate is lower in nondense versus dense breasts. CAD may be particularly advantageous in patients with dense breasts, in which mammography is most challenging.     

Soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary findings.

PURPOSE: To assess the performance of radiologists in the detection of masses and microcalcification clusters on digitized mammograms by using different computer-assisted detection (CAD) cuing environments. MATERIALS AND METHODS: Two hundred nine digitized mammograms depicting 57 verified masses and 38 microcalcification clusters in 85 positive and 35 negative cases were interpreted independently by seven radiologists using five display modes. Except for the first mode, for which no CAD results were provided, suspicious regions identified with a CAD scheme were cued in all the other modes by using a combination of two cuing sensitivities (90% and 50%) and two false-positive rates (0.5 and 2.0 per image). A receiver operating characteristic study was performed by using soft-copy images. RESULTS: CAD cuing at 90% sensitivity and a rate of 0.5 false-positive region per image improved observer performance levels significantly (P < .01). As accuracy of CAD cuing decreased so did observer performances (P < .01). Cuing specificity affected mass detection more significantly, while cuing sensitivity affected detection of microcalcification clusters more significantly (P < .01). Reduction of cuing sensitivity and specificity significantly increased false-negative rates in noncued areas (P < .05). Trends were consistent for all observers. CONCLUSION: CAD systems have the potential to significantly improve diagnostic performance in mammography. However, poorly performing schemes could adversely affect observer performance in both cued and noncued areas.     

Influence of breast lesion size and histologic findings on tumor detection rate of a computer-aided detection system

PURPOSE: To evaluate associations between histopathologic findings, tumor size, and detection rate of malignant mammographic findings by using a computer-aided detection (CAD) system. MATERIALS AND METHODS: The study included 208 mammographically detected histologically proven malignant breast lesions in 208 women. Findings were 150 masses and 114 microcalcifications; 56 lesions showed both findings; 94 lesions, mass only; and 58 lesions, microcalcification only. CAD was used to evaluate mammograms in two views retrospectively. Also, corresponding histopathologic findings and lesion size were evaluated. CAD marks were considered positive if, on at least one view, they correctly identified the corresponding mammographic lesion location. RESULTS: Ninety percent (135 of 150) of masses and 93.0% (106 of 114) of microcalcifications were marked correctly by the CAD system. Overall tumor detection rate was 93.8% (195 of 208). Size-related detection rate for masses was 83.3% (25 of 30) for lesions up to 10 mm, 100% (45 of 45) for lesions 11-20 mm, 100% (46 of 46) for lesions 21-30 mm, 83.3% (10 of 12) for lesions 31-40 mm, and 52.9% (nine of 17) for lesions larger than 40 mm. Size-related tumor detection rate for microcalcifications was 92.5% (37 of 40) for microcalcifications up to 10 mm, 93.1% (27 of 29) for lesions 11-20 mm, 100% (20 of 20) for lesions 21-30 mm, 87.5% (seven of eight) for lesions 31-40 mm, and 88.2% (15 of 17) for larger microcalcifications. Detection rates for mammographically visible masses (invasive ductal carcinoma, invasive lobular carcinoma, invasive tubular carcinoma, noninvasive cancers, mucinoid cancers, and others) were 92.3% (84 of 91), 89.3% (25 of 28), 75.0% (six of eight), 100% (15 of 15), 33.3% (one of three), and 80.0% (four of five), respectively. Detectability rates for mammographically visible areas suspicious for microcalcifications (invasive ductal carcinoma, invasive lobular carcinoma, invasive tubular carcinoma, and noninvasive cancers) were 92.3% (60 of 65), 100% (eight of eight), 100% (five of five), and 91.9% (31 of 34), respectively. Highest overall detection rates were observed for invasive ductal carcinomas (96.6% [112 of 116]) and noninvasive cancers (92.9% [39 of 42]). CONCLUSION: Highest detection rates were observed for 10-30-mm tumor masses and for invasive ductal carcinomas and noninvasive cancers.     

Radiologist detection of microcalcifications with and without computer-aided detection: a comparative study

AIM: To compare the sensitivity and specificity of microcalcification detection by radiologists alone and assisted by a computer-aided detection (CAD) system. MATERIALS AND METHODS: Films of 106 patients were masked, randomized, digitized and analysed by the CAD-system. Five readers interpreted the original mammograms and were blinded to demographics, medical history and earlier films. Forty-two mammograms with malignant microcalcifications, 40 with benign microcalcifications and 24 normal mammograms were included. Results were recorded on a standardized image interpretation form. The mammograms with suspicious areas flagged by the CAD-system were displayed on mini-monitors and immediately re-reviewed. The interpretation was again recorded on a new copy of the standard form and classified according to six groups. RESULTS: Forty-one out of 42 (98%) malignant microcalcifications and 32 of 40 (80%) benign microcalcifications were flagged by the CAD-system. There was an average of 1.2 markers per image. The sensitivity for malignant microcalcifications detection by mammographers without and with the CAD-system ranged from 81% to 98% and from 88% to 98%, respectively. The mean difference without and with CAD-system was 2.2% (range 0-7%). CONCLUSION: No statistically significant changes in sensitivity were found when experienced mammographers were assisted by the CAD-system, with no significant compromise in specificity.     

Screening mammography-detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms

PURPOSE: To retrospectively evaluate the sensitivity of the performance of a computer-aided detection (CAD) system applied to full-field digital mammograms for detection of breast cancers in a screening group, with histologic findings as the reference standard. MATERIALS AND METHODS: This study had institutional review board approval, and patient informed consent was waived. A commercially available CAD system was applied to the digital mammograms of 103 women (mean age, 51 years; range, 35-69 years) with 103 breast cancers detected with screening. Sensitivity values of the CAD system according to mammographic appearance, breast composition, and histologic findings were analyzed. Normal mammograms from 100 women (mean age, 54 years; age range, 35-75 years) with no mammographic and clinical abnormality during 2-year follow-up were used to determine false-positive CAD system marks. Differences between the cancer detection rates in fatty and dense breasts for the CAD system were compared by using the chi(2) test. RESULTS: The CAD system correctly marked 99 (96.1%) of 103 breast cancers. The CAD system marked all 44 breast cancers that manifested as microcalcifications only, all 23 breast cancers that manifested as a mass with microcalcifications, and 32 (89%) of 36 lesions that appeared as a mass only. The sensitivity of the CAD system in the fatty breast group was 95% (59 of 62) and in the dense breast group was 98% (40 of 41) (P = .537). The CAD system correctly marked all 31 lesions of ductal carcinoma in situ (DCIS), all 22 lesions of invasive ductal carcinoma with DCIS, the single invasive lobular carcinoma lesion, and 45 (92%) of 49 lesions of invasive ductal carcinoma. On normal mammograms, the mean number of false-positive marks per patient was 1.80 (range, 0-10 marks; median, 1 mark). CONCLUSION: The CAD system can correctly mark most (96.1%) asymptomatic breast cancers detected with digital mammographic screening, with acceptable false-positive marks (1.80 per patient).     

Applying computer-assisted detection schemes to digitized mammograms after JPEG data compression: an assessment

RATIONALE AND OBJECTIVES: The authors' purpose was to assess the effects of Joint Photographic Experts Group (JPEG) image data compression on the performance of computer-assisted detection (CAD) schemes for the detection of masses and microcalcification clusters on digitized mammograms. MATERIALS AND METHODS: This study included 952 mammograms that were digitized and compressed with a JPEG-compatible image-compression scheme. A CAD scheme, previously developed in the authors' laboratory and optimized for noncompressed images, was applied to reconstructed images after compression at five levels. The performance was compared with that obtained with the original noncompressed digitized images. RESULTS: For mass detection, there were no significant differences in performance between noncompressed and compressed images for true-positive regions (P = .25) or false-positive regions (P = .40). In all six modes the scheme identified 80% of masses with less than one false-positive region per image. For the detection of microcalcification clusters, there was significant performance degradation (P < .001) at all compression levels. Detection sensitivity was reduced by 4%-10% as compression ratios increased from 17:1 to 62:1. At the same time, the false-positive detection rate was increased by 91%-140%. CONCLUSION: The JPEG algorithm did not adversely affect the performance of the CAD scheme for detecting masses, but it did significantly affect the detection of microcalcification clusters.     

Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments

RATIONALE AND OBJECTIVES: The authors evaluated the impact of different computer-aided detection (CAD) cueing conditions on radiologists' performance levels in detecting and classifying masses depicted on mammograms. MATERIALS AND METHODS: In an observer performance study, eight radiologists interpreted 110 subtle cases six times under different display conditions to detect depicted masses and classify them as benign or malignant. Forty-five cases depicted biopsy-proven masses and 65 were negative. One mass-based cueing sensitivity of 80% and two false-positive cueing rates of 1.2 and 0.5 per image were used in this study. In one mode, radiologists first interpreted images without CAD results, followed by the display of cues and reinterpretation. In another mode, radiologists viewed CAD cues as images were presented and then interpreted images. Free-response receiver operating characteristic method was used to analyze and compare detection performance. The receiver operating characteristic method was used to evaluate classification performance. RESULTS: At these performance levels, providing cues after initial interpretation had little effect on the overall performance in detecting masses. However, in the mode with the highest false-positive cueing rate, viewing CAD cues immediately upon display of images significantly reduced average performance for both detection and classification tasks (P < .05). Viewing CAD cues during the initial display consistently resulted in fewer abnormalities being identified in noncued regions. CONCLUSION: CAD systems with low sensitivity (< or = 80% on mass-based detection) and high false-positive rate (> or = 0.5 per image) in a dataset with subtle abnormalities had little effect on radiologists' performance in the detection and classification of mammographic masses.     

Reproducibility of Computer-Aided Detection Marks in Digital Mammography

Objective

To evaluate the performance and reproducibility of a computer-aided detection (CAD) system in mediolateral oblique (MLO) digital mammograms taken serially, without release of breast compression.

Materials and Methods

A CAD system was applied preoperatively to the full-field digital mammograms of two MLO views taken without release of breast compression in 82 patients (age range: 33 - 83 years; mean age: 49 years) with previously diagnosed breast cancers. The total number of visible lesion components in 82 patients was 101: 66 masses and 35 microcalcifications. We analyzed the sensitivity and reproducibility of the CAD marks.

Results

The sensitivity of the CAD system for first MLO views was 71% (47/66) for masses and 80% (28/35) for microcalcifications. The sensitivity of the CAD system for second MLO views was 68% (45/66) for masses and 17% (6/35) for microcalcifications. In 84 ipsilateral serial MLO image sets (two patients had bilateral cancers), identical images, regardless of the existence of CAD marks, were obtained for 35% (29/84) and identical images with CAD marks were obtained for 29% (23/78). Identical images, regardless of the existence of CAD marks, for contralateral MLO images were 65% (52/80) and identical images with CAD marks were obtained for 28% (11/39). The reproducibility of CAD marks for the true positive masses in serial MLO views was 84% (42/50) and that for the true positive microcalcifications was 0% (0/34).

Conclusion

The CAD system in digital mammograms showed a high sensitivity for detecting masses and microcalcifications. However, reproducibility of microcalcification marks was very low in MLO views taken serially without release of breast compression. Minute positional change and patient movement can alter the images and result in a significant effect on the algorithm utilized by the CAD for detecting microcalcifications.     

Computer-assisted detection as a second reader in symptomatic Asian women with palpable breast cancer

Purpose: To determine the potential role of a computer-assisted detection (CAD) algorithm as a second reader for experienced and inexperienced radiologists in mammography reading in Asian women.

Material and Methods: Two-view mammograms performed in 124 consecutive patients who presented with palpable breast cancer masses were retrospectively evaluated by two experienced breast radiologists (7 and 10 years' experience). The original reports of the session radiologists with variable experience of reading mammograms (2 to more than 10 years) were also evaluated. The number of suspicious masses and microcalcification clusters detected in each patient by both groups of radiologists were recorded. The radiologists then re-evaluated the films with the CAD system as a second reader. Any improvement in the detectability of breast pathology by either the experienced radiologists and/or by the session radiologists was then assessed. A total of 127 breasts had biopsy-proven carcinoma; 74 breasts had mastectomy performed. All the imaging results were correlated with tru-cut biopsy or mastectomy histology.

Results: With CAD-aided interpretation, there were altogether 95 visible masses and 77 suspicious microcalcification clusters in 109 mammographically detectable cancers correlated with histology results. There was a 7.4% (7/95) and 10.4% (8/77) increase in the number of masses and microcalcification clusters detected, respectively, by the experienced radiologists after application of CAD, whereas the increase was 13.7% (13/95) and 27.3% (21/77) for detection of masses and microcalcifications by the session radiologists, respectively. In 9 patients, a secondary focus detected by CAD was confirmed by histology. Three patients had contralateral breast tumors, 1 had a satellite invasive tumor while 5 had ductal carcinoma in situ on the same breast. Based on the biopsies and 74 mastectomies, the true-positive and false-positive detection rate of CAD was 92.6% and 31.8% for detection of carcinomas. The true-positive and false-positive detection rates were 100% and 58.8% for microcalcification clusters.

Conclusion: The current generation CAD algorithm helped to improve the detection rate of carcinomas, calcifications and multifocality in Asian breasts.     

Evaluation of computer-aided detection systems in the detection of small invasive breast carcinoma

PURPOSE: To retrospectively compare two CAD systems for detecting invasive breast cancers manifesting as noncalcified masses smaller than 16 mm. MATERIALS AND METHODS: Waiver of informed consent was granted by the Institutional Review Board that approved this HIPAA-compliant study. Mammograms obtained from two institutions providing consecutive invasive carcinomas manifesting as noncalcified masses smaller than 16 mm were evaluated by using two commercially available CAD systems (R2 ImageChecker M1000, version 5.0A and iCAD Second Look, version 6.0 mid operating point). To provide statistical power to test for a possible 10% difference in the sensitivity performance between the systems, 192 consecutive mammographic studies (182 unifocal, six multifocal, and four bilateral cancers) were collected. Masses were characterized using the Breast Imaging Reporting and Data System (BI-RADS). Per study specificity and mass false marker rate were determined by using 51 normal four-view studies, while scoring only the mass false-positive marks for noncalcified masses. Associations between mass characteristics and supplying institution were compared by using chi2 tests. A P value of .05 was considered to indicate a significant difference. RESULTS: The respective per study sensitivity, per image (ie, per view) sensitivity, per study specificity, and mass false-positive marker rates were 81.8%, 64.7%, 39.2%, and 1.08 for the R2 ImageChecker M1000 system, and 60.9%, 42.6%, 31.4%, and 1.41 for the iCAD Second Look system. The overall per study and per image sensitivities were significantly better for R2 than for iCAD (McNemar test, all P<.001), with a nonsignificant higher per study specificity and lower mass false marker rate on normal studies. CAD results demonstrated at least a 20% variation between BI-RADS categories 4a and 5 for per study and per image sensitivity. CONCLUSION: A statistically significant difference was observed in per study and per image sensitivity in our mammography data set with small (<16 mm), noncalcified invasive breast malignancies between two CAD systems. Differences in per study specificity and mass false marker rate were noted but were not statistically significant.     

Computer-aided detection of microcalcifications in mammograms. Methodology and preliminary clinical study

The authors investigated the feasibility of using computer methods for automated detection of clustered microcalcifications on clinical mammograms. A new difference-image approach using a matched filter/box-rim filter combination effectively removed the structured background from the image. A locally adaptive gray-level thresholding technique was then used for extraction of the signals from the resulting difference image. Signal-extraction criteria based on the size, contrast, number, and clustering properties of microcalcifications were next imposed on the detected signals to distinguish true signals from noise or artifacts. The detection accuracy of the computer scheme was evaluated by means of a free response receiver operating characteristic (FROC) analysis. It was found that, for simulated subtle microcalcifications superimposed on normal mammograms, the difference-image approach with a matched filter/box-rim filter combination could yield a true-positive cluster detection rate of 80% at a false-positive detection rate of one cluster per image. In a study of 20 clinical images containing moderately subtle microcalcifications, the automated computer scheme obtained an 82% true-positive cluster detection rate at a false-positive detection rate of one cluster per image. These results indicate that the automated method has the potential to aid radiologists in screening mammograms for clustered microcalcifications.     

Computer-aided diagnosis of masses with full-field digital mammography

RATIONALE AND OBJECTIVES: The authors developed and evaluated a method of computer-aided diagnosis (CAD) for mass detection with full-field digital mammography (FFDM). MATERIALS AND METHODS: The new CAD method for FFDM employs adaptive, nonlinear multiscale processing and hybrid classification methods. The major strategies are (a) to "standardize" the mammographic image before it is input to the analysis modules, (b) to adapt the segmentation of suspicious regions adapt to accommodate different characteristics of masses and mammograms, and (c) to use combined "hard" and "soft" decision making in discriminating between mass and normal tissue regions. Two data sets of diagnostic FFDM mammograms were used. The training data set includes 36 normal and 24 abnormal mammograms (34 masses), and the testing data set includes 24 normal and 10 abnormal mammograms (10 masses). The tumors in this diagnostic database were more subtle and difficult to detect than those in screening databases the authors have used before. RESULTS: With the limited database and a partial optimization, a sensitivity of 91% was obtained in training, with a false-positive rate of 3.21 per image. At this trained operating point of the CAD system, six of 10 subtle masses were detected in testing. CONCLUSION: The CAD algorithms developed in screen-film mammography can be modified for FFDM. More data analysis and system optimization and evaluation will be needed before CAD can be integrated efficiently into the performance of FFDM.     

Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis

Relatively simple, but important, detection tasks in radiology are nearing accessibility to computer-aided diagnostic (CAD) methods. The authors have studied one such task, the detection of clustered microcalcifications on mammograms, to determine whether CAD can improve radiologists' performance under controlled but generally realistic circumstances. The results of their receiver operating characteristic (ROC) study show that CAD, as implemented by their computer code in its present state of development, does significantly improve radiologists' accuracy in detecting clustered microcalcifications under conditions that simulate the rapid interpretation of screening mammograms. The results suggest also that a reduction in the computer's false-positive rate will further improve radiologists' diagnostic accuracy, although the improvement falls short of statistical significance in this study.     

Computer-aided detection in mammography: an assessment of performance on current and prior images

RATIONALE AND OBJECTIVES: The authors assessed and compared the performance of a computer-aided detection (CAD) scheme for the detection of masses and microcalcification clusters on a set of images collected from two consecutive ("current" and "prior") mammographic examinations. MATERIALS AND METHODS: A previously developed CAD scheme was used to assess two consecutive screening mammograms from 200 cases in which the current mammogram showed a mass or cluster of microcalcifications that resulted in breast biopsy. The latest prior examinations had been initially interpreted as negative or definitely benign findings (Breast Imaging Reporting and Data System rating, 1 or 2). The study involved images of 400 examinations acquired in 200 patients. Radiologists identified 172 masses and 128 clusters of microcalcifications on the current images. The performance of the CAD scheme was analyzed and compared for the current and latest prior images. RESULTS: There were significant differences (P < .01) between current and prior images in many feature values. The performance of the CAD scheme was significantly lower for prior than for current images (P < .01). At 0.5 and 0.2 false-positive mass and cluster cues per image, the scheme detected 78 malignant masses (78%) and 63 malignant clusters (80%) on current images. Only 42% of malignant cases were detected on prior images, including 40 masses (40%) and 36 microcalcification clusters (46%). CONCLUSION: CAD schemes can detect a substantial fraction of masses and microcalcification clusters depicted on prior images. To improve performance with prior images, the scheme may have to be adaptively reoptimized with increasingly more subtle abnormalities.     

Breast cancer detection: evaluation of a mass-detection algorithm for computer-aided diagnosis -- experience in 263 patients

PURPOSE: To evaluate the performance of a computer-aided diagnosis (CAD) mass-detection algorithm in marking preoperative masses. MATERIALS AND METHODS: Digitized mammograms were processed with an adaptive enhancement filter followed by a local border refinement stage. Features were then extracted from each detected structure and used to identify potential masses. The performance of the algorithm was evaluated in independent cases obtained from 263 patients from two institutions. Each case contained one or more pathologically proved breast masses. Contralateral mammograms obtained in the same patients that did not contain a visible lesion were used to estimate the CAD marker rate for the algorithm. The tradeoff between detection sensitivity and the number of CAD marks was analyzed in this study. RESULTS: Malignant masses were detected with the computer in 87% (135 of 156), 83% (130 of 156), and 77% (120 of 156) of the malignant cases at CAD marker rates of 1.5, 1.0, and 0.5 marks per mammogram, respectively. The difference between malignant mass-detection performance in subsets of cases collected at each institution was found to be less than 1%. The detection accuracy for benign masses was lower than that for malignant masses. CONCLUSION: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.     

The performance of computer-aided detection when analyzing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection

Purpose

The clinical role of CAD systems to detect breast cancer, which have not been on cancer containing mammograms not detected by the radiologist was proven retrospectively.

Methods

All patients from 1992 to 2005 with a histologically verified malignant breast lesion and a mammogram at our department, were analyzed in retrospect focussing on the time of detection of the malignant lesion. All prior mammograms were analyzed by CAD (CADx, USA). The resulting CAD printout was matched with the cancer containing images yielding to the radiological diagnosis of breast cancer. CAD performance, sensitivity as well as the association of CAD and radiological features were analyzed.

Results

278 mammograms fulfilled the inclusion criteria. 111 cases showed a retrospectively visible lesion (71 masses, 23 single microcalcification clusters, 16 masses with microcalcifications, in one case two microcalcification clusters). 54/87 masses and 34/41 microcalcifications were detected by CAD.Detection rates varied from 9/20 (ACR 1) to 5/7 (ACR 4) (45% vs. 71%). The detection of microcalcifications was not influenced by breast tissue density.

Conclusion

CAD might be useful in an earlier detection of subtle breast cancer cases, which might remain otherwise undetected.     

Evaluation of a wavelet-based computer-assisted detection system for identifying microcalcifications in digital full-field mammography

PURPOSE: To evaluate a new wavelet-based computer-assisted detection (CAD) system for detecting and enhancing microcalcifications. MATERIAL AND METHODS: A total of 280 mammograms acquired by full-field digital mammography (Senographe 2000D; G.E. Medical Systems Milwaukee, Wisc., USA) were analyzed with and without a new wavelet-based CAD system for detecting and enhancing microcalcifications. The mammograms comprised roughly equal numbers of cases from each of the BIRADS (Breast Imaging, Reporting and Data System, according to the American College of Roentgenology) categories 1-5. Histologic confirmation was available for all of the 180 cases assigned BIRADS categories 3-5. Four readers interpreted all 280 images for suspicious microcalcifications using a scale of 1-5. The readers alternately assessed 5 images with and 5 without CAD. In a second reading immediately following the first, the readers had to reassess the 280 mammograms. The images that had already been interpreted without CAD were now presented with CAD and vice versa. The images were interpreted as soft copies on a diagnostic mammography workstation (Image Diagnost GmbH, Neufahrn/Munich, Germany). All images interpreted with CAD were presented with enhancement of microcalcifications by wavelet algorithms and prompting of microcalcifications. ROC (receiver operating characteristic) analyses were performed, and image interpretation time with and without CAD was measured. RESULTS: The overall time for interpretation required by all 4 readers together was 483 min with CAD compared to 580 min without CAD. ROC analysis revealed no significant advantage of CAD for the individual readers. Readers 3 (0.811/0.817) and 4 (0.799/0.843) had a slightly improved AUC (area under the curve) with CAD. Readers 1 and 2 had a slightly lower AUC with CAD (0.832 versus 0.861 and 0.818 versus 0.849). CONCLUSION: The CAD system significantly (P<0.05, t test) speeded up image interpretation with respect to the identification of microcalcifications, while the diagnostic quality remained almost identical under the study conditions.