Effect of soft-copy display supported by CAD on mammography screening performance.

Contains fulltext : 50237.pdf (publisher's version ) (Closed access)Diagnostic performance and reading speed for conventional mammography film reading is compared to reading digitized mammograms on a dedicated workstation. A series of mammograms judged negative at screening and corresponding priors were collected. Half were diagnosed as cancer at the next screening, or earlier for interval cancers. The others were normal. Original films were read by fifteen experienced screening radiologists. The readers annotated potential abnormalities and estimated their likelihood of malignancy. More than 1 year later, five radiologists reread a subset of 271 cases (88 cancer cases having visible signs in retrospect and 183 normals) on a mammography workstation after film digitization. Markers from a computer-aided detection (CAD) system for microcalcifications were available to the readers. Performance was evaluated by comparison of A(z)-scores based on ROC and multiple-Reader multiple-case (MRMC) analysis, and localized receiver operating characteristic (LROC) analysis for the 271 cases. Reading speed was also determined. No significant difference in diagnostic performance was observed between conventional and soft-copy reading. Average A(z)-scores were 0.83 and 0.84 respectively. Soft-copy reading was only slightly slower than conventional reading. Using a mammography workstation including CAD for detection of microcalcifications, soft-copy reading is possible without loss of quality or efficiency

Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick’s texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases