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.

Purpose:To assess the effect of using computer-aided detection (CAD) in second-read mode on readers' accuracy in interpreting computed tomographic (CT) colonographic images. Materials and Methods:The contributing institutions performed the examinations under approval of their local institutional review board, with waiver of informed consent, for this HIPAA-compliant study. A cohort of 100 colonoscopy-proved cases was used: In 52 patients with fi ndings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with fi ndings negative for polyps, no polyps were found. Nineteen blinded readers interpreted each case at two different times, with and without the assistance of a commercial CAD system. The effect of CAD was assessed in segment-level and patient-level receiver operating characteristic (ROC) curve analyses. Results:Thirteen (68%) of 19 readers demonstrated higher accuracy with CAD, as measured with the segment-level area under the ROC curve (AUC). The readers' average segment-level AUC with CAD (0.758) was signifi cantly greater ( P = .015) than the average AUC in the unassisted read (0.737). Readers' per-segment, per-patient, and per-polyp sensitivity for all polyps of 6 mm or larger was higher ( P , .011, .007, .005, respectively) for readings with CAD compared with unassisted readings (0.517 versus 0.465, 0.521 versus 0.466, and 0.477 versus 0.422, respectively). Sensitivity for patients with at least one large polyp of 10 mm or larger was also higher ( P , .047) with CAD than without (0.777 versus 0.743). Average reader sensitivity also improved with CAD by more than 0.08 for small adenomas. Use of CAD reduced specifi city of readers by 0.025 ( P = .05). Conclusion:Use of CAD resulted in a signifi cant improvement in overall reader performance. CAD improves reader sensitivity when measured per segment, per patient, and per polyp for small polyps and adenomas and also reduces specifi city by a small amount.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup /suppl

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A new model-based vision (MBV) algorithm is developed to find regions of interest (ROI's) corresponding to masses in digitized mammograms and to classify the masses as malignant/benign. The MBV algorithm is comprised of five modules to structurally identify suspicious ROI's, eliminate false positives, and classify the remaining as malignant or benign. The focus of attention module uses a difference of Gaussians (DoG) filter to highlight suspicious regions in the mammogram. The index module uses tests to reduce the number of nonmalignant regions from 8.39 to 2.36 per full breast image. Size, shape, contrast, and Laws texture features are used to develop the prediction module's mass models. Derivative-based feature saliency techniques are used to determine the best features for classification. Nine features are chosen to define the malignant/benign models. The feature extraction module obtains these features from all suspicious ROI's. The matching module classifies the regions using a multilayer perceptron neural network architecture to obtain an overall classification accuracy of 100% for the segmented malignant masses with a false-positive rate of 1.8 per full breast image. This system has a sensitivity of 92% for locating malignant ROI's. The database contains 272 images (12 b, 100 microm) with 36 malignant and 53 benign mass images. The results demonstrate that the MBV approach provides a structured order of integrating complex stages into a system for radiologists.

Cytology has been reported to have suboptimal sensitivity for detecting pancreatobiliary tract cancer in biliary tract specimens partly as a result of low specimen cellularity and obscuring noncellular components. The goal of this study was to determine if the use of a glacial acetic acid wash prior to processing would increase the cellularity and improve the quality of ThinPrep® slides when compared to standard non‐gyn ThinPrep processing. Fifty consecutive pancreatobiliary tract specimens containing 20 ml of sample/PreservCyt® were divided equally for standard non‐gyn ThinPrep (STP) and glacial acetic acid ThinPrep processing (GATP). A manual drop preparation was also performed on residual STP specimen to determine the number of cells left in the vial during STP processing. Twenty‐six (52%) specimens had more epithelial cell groupings with the GATP methodology while 19 (38%) had equivalent cellularity with both methods. The STP method produced more epithelial cell groupings in 5 (10%) of the specimens. Of the 26 specimens that had less cells with the STP method, 14 (54%) had ≥50 cell groupings on the manual drop slide processed from the residual STP specimen suggesting that many cells remain in the vial after STP processing. The GATP method was preferred in 25 (50%) of the specimens, the STP method in 5 (10%), while both methodologies provided similar findings in the remaining 20 (40%) of specimens. The data from this study suggests that the GATP method results in more cells being placed on the slide and was preferred over the STP method in a majority of specimens. Diagn. Cytopathol. 2010;38:627–632. © 2009 Wiley‐Liss, Inc.