Robustness of computerized identification of masses in digitized mammograms. A preliminary assessment

CAD for mammography: the technique, results, current role and further developments

CAD systems, developed to assist the radiologist in the detection of suspicious lesions on mammograms, are currently controversially discussed. The highly sensitive detection of malignant structures including priors by CAD is linked with a low specific performance and a high rate of falsely positive markings. This causes controversial results regarding the effect of CAD systems for the diagnosing radiologist. This review aims to give an overview of the current literature, to state the currently discussed controversial results of CAD and to give an outlook on the next developments, which are not limited to senology, but include many other applications of CAD systems in radiology.

Knowledge-based computer-aided detection of masses on digitized mammograms: a preliminary assessment

The purpose of this work was to develop and evaluate a computer-aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge-based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule-based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring "similarity" between a suspicious region and a "known" mass, then deriving a composite "likelihood" measure based on all "known" masses to determine the state of the suspicious region. To assess the performance of this method, receiver-operating characteristic (ROC) analyses were employed. Using a leave-one-out validation method with the development set of 600 regions, the knowledge-based CAD scheme achieved an area under the ROC curve of 0.83. Fifty-one percent of the previously identified false-positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1,000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge-based approaches can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule-based CAD schemes.

Reproducibility--an important factor determining the quality of computer-aided detection (CAD) systems

PURPOSE

To test the reproducibility of markings on mammography films set by a commercial computer aided detection (CAD) system.

PATIENTS AND METHODS

One hundred unilateral mammography examinations (each in CC and MLO) of 100 patients with mammographically detected suspicious foci, which were histopathologically proven to be malignant, were scanned three times with the CAD system, retrospectively. Every fifth patient of the institutional tumor case sampler was enrolled in the study. Only cases with one visible lesion were included in the study. Reproducibility and sensitivity (in both the strict and the broader sense) were determined. Strict sensitivity means the correct set of markers in both images, whereas broader sensitivity means the correct set in at least one of the images. Sixteen of 100 malignancies were indicated by focal suspicious microcalcification clusters, 53 tumors by masses and 31 cases by both signs of breast cancer. The CAD evaluation was divided into only two different markers: one for microcalcifications and one for masses. Thus, 47 (16+31) tumor-induced microcalcifications and 84 (53+31) malignancy-related masses were checked using the CAD system.

RESULTS

Eighteen of 100 unilateral mammography examinations revealed identical patterns in all three scans (18% reproducibility). Eleven of 47 suspicious focal microcalcification clusters and 43/84 masses were correctly marked on both mammographic views in all three CAD scans (strict and broader sensitivity, 23.4 and 51.1%, respectively). Six of 47 microcalcification clusters and 8/84 masses were totally missed in all images by the system (false negative rate, 12.8 and 9.6%, respectively).

CONCLUSION

Reproducibility is essential for CAD systems. Currently, reproducibility of the used CAD system appears to be insufficient for clinical routine. Improvement of the system characteristics would make such systems valuable as a 'second reader' in clinical examination.

Computer-aided detection of clustered microcalcifications on digitized mammograms: a robustness experiment

RATIONALE AND OBJECTIVES

The authors assessed the performance of an existing computer-aided diagnosis (CAD) scheme for the detection of clustered microcalcifications in a large image database.

METHODS

A previously developed, rule-based system was used to assess detectability of microcalcification clusters in a set of 386 digitized mammograms with 239 verified clusters visible on 191 images. The test was performed without any reoptimization of the scheme. None of the 386 images had been used in any previous scheme development or testing procedures.

RESULTS

The CAD scheme achieved 89.5% sensitivity at an average false-positive detection rate of 0.39 per image. In 75% of all images, no false-positive findings occurred. Twenty-three of 25 false-negative findings (misses) occurred during the last two stages in the detection process.

CONCLUSION

This scheme produced reasonable results in a large data set of images with a large variety of cluster characteristics.