Computer-aided Detection Performance Research Articles

CAD-Aided Mammogram Training 1

Although computer-aided detection (CAD) improves the diagnosis rate of early breast cancer, it has not been well integrated into radiology residency and technician training program. Moreover, CAD performance studies ignore the reader's training and experience with CAD. The purpose of this study was to investigate whether CAD training via a cognitive-perceptual based hypermedia program has effects on the performance studies of mammogram reading. Three observers read a pretest set of 80 breast cancer cases (43 negative, 23 benign, and 14 malignant cancer cases). During 4 weeks' training, the observers used a hypermedia instructional program in CAD-aided mammography interpretation. The program includes modules of CAD attention-focusing schemes, CAD procedural knowledge, and case-based simulations in mammography interpretation in consensus with CAD. By the end of the fourth week of the training, they reviewed a posttest set of cases. Data were analyzed with multireader, multicase receiver operating characteristic methods. Three readers performed better in mammogram reading after training in CAD knowledge than they did before CAD training. CAD training and experience improved the performance of CAD-aided mammography interpretation. A statistically significant difference was found in each observer's performance in CAD-aided mammogram reading before and after the training. CAD training will influence the perception, recognition, and interpretation of early breast cancer and CAD performance studies. Furthermore, the young generation of radiologic professionals can have more training in various attention-focusing features, declarative knowledge, procedural knowledge, and conditional knowledge of CAD and incorporate them into their knowledge base and strategic processing for the purpose of improving the accuracy of mammography interpretation performance.

A method to test the reproducibility and to improve performance of computer‐aided detection schemes for digitized mammograms

The purpose of this study is to develop a new method for assessment of the reproducibility of computer-aided detection (CAD) schemes for digitized mammograms and to evaluate the possibility of using the implemented approach for improving CAD performance. Two thousand digitized mammograms (representing 500 cases) with 300 depicted verified masses were selected in the study. Series of images were generated for each digitized image by resampling after a series of slight image rotations. A CAD scheme developed in our laboratory was applied to all images to detect suspicious mass regions. We evaluated the reproducibility of the scheme using the detection sensitivity and false-positive rates for the original and resampled images. We also explored the possibility of improving CAD performance using three methods of combining results from the original and resampled images, including simple grouping, averaging output scores, and averaging output scores after grouping. The CAD scheme generated a detection score (from 0 to 1) for each identified suspicious region. A region with a detection score >0.5 was considered as positive. The CAD scheme detected 238 masses (79.3% case-based sensitivity) and identified 1093 false-positive regions (average 0.55 per image) in the original image dataset. In eleven repeated tests using original and ten sets of rotated and resampled images, the scheme detected a maximum of 271 masses and identified as many as 2359 false-positive regions. Two hundred and eighteen masses (80.4%) and 618 false-positive regions (26.2%) were detected in all 11 sets of images. Combining detection results improved reproducibility and the overall CAD performance. In the range of an average false-positive detection rate between 0.5 and 1 per image, the sensitivity of the scheme could be increased approximately 5% after averaging the scores of the regions detected in at least four images. At low false-positive rate (e.g., < or =average 0.3 per image), the grouping method alone could increase CAD sensitivity by 7%. The study demonstrated that reproducibility of a CAD scheme can be tested using a set of slightly rotated and resampled images. Because the reproducibility of true-positive detections is generally higher than that of false-positive detections, combining detection results generated from subsets of rotated and resampled images could improve both reproducibility and overall performance of CAD schemes.

Computer-aided detection performance in mammographic examination of masses: assessment.

To compare performance of two computer-aided detection (CAD) systems and an in-house scheme applied to five groups of sequentially acquired screening mammograms. Two hundred nineteen film-based mammographic examinations, classified into five groups, were included in this study. Group 1 included 58 examinations in which verified malignant masses were detected during screening; group 2, 39 in which all available latest examinations were performed prior to diagnosis of these malignant masses (subset of 39 women from group 1); group 3, 22 in which findings were interpreted as negative but were verified as cancer within 1 year from the negative interpretation (missed cancers); group 4, 50 in which findings were negative and patients were not recalled for additional procedures; and group 5, 50 in which patients were recalled for additional procedures and findings were negative for cancer. In all examinations, images were processed with two Food and Drug Administration-approved commercially available CAD systems and an in-house scheme. Performance levels in terms of true-positive detection rates and number of false-positive identifications per image and per examination were compared. Mass detection rates in positive examinations (group 1) were 67%-72%. Detection rates among three systems were not significantly different (P > .05). In 50 negative screening examinations (group 4), false-positive rates ranged from 1.08 to 1.68 per four-view examination. Performance level differences among systems were significant for false-positive rates (P = .008). Performance of all systems was at levels lower than publicly suggested in some retrospective studies. False-positive CAD cueing rates were significantly higher for negative examinations in which patients were recalled (group 5) than they were for those in which patients were not recalled (group 4) (P < or = .002). Performance of CAD systems for mass detection at mammography varies significantly, depending on examination and system used. Actual performance of all systems in clinical environment can be improved.

Computer-aided detection for mammography: improved algorithm performance with operator determined points characterized by new metrics

Recent prospective studies have suggested that user's confidence in computer-aided detection (CAD) marks and their tolerance of false positive (FP) marks play a role in the benefit gained from a CAD device. In this paper, we propose to (1) introduce new metrics to improve the characterization of the latest generation of CAD algorithms and (2) to describe the algorithm performance tradeoffs with user adjustable operating points. The performance of the CAD algorithm (V8.0, R2 Technology, Sunnyvale, CA) with user adjustable operating points was assessed on a multi-institutional database of 832 consecutive, biopsy-proven cancers detected by screening mammography and 345 clinically confirmed normal (four view) cases. The standard measurements of CAD performance were made—case sensitivity for microcalcifications and masses, and false positive (FP) marker rate on normal cases. In addition, new measurements of case specificity (the percentage of normal cases with no CAD marks) and two-view sensitivity (the percentage of cancer cases with a cancer visible ( n=628) and marked on both views) were used. It is likely that the case specificity measure reflects the impact of the CAD algorithm on the workflow of the radiologist. Further, a high value for the two-view sensitivity is likely to increase the radiologist's confidence in the CAD tool. This new CAD system allows the user to choose an operating point to best fit their clinical requirements.

Performance of Computer-aided Detection in False-Negative Screening Mammograms of Breast Cancers

Performance of Computer-aided Detection in False-Negative Screening Mammograms of Breast Cancers

Performance of Computer-aided Detection in False-Negative Screening Mammograms of Breast Cancers

Performance of Computer-aided Detection in False-Negative Screening Mammograms of Breast Cancers