Significance of enhanced cerebral gray-white matter contrast at 80 kVp compared to conventional 120 kVp CT scan in the evaluation of acute stroke.

We aimed to determine whether 80 kVp conventional nonenhanced head CT scans have better gray-white matter contrast than standard 120 kVp scans performed on the same patients. Thirty head CT scans acquired at 80 kVp (CT dose index [CTDI]vol 46) were compared to prior studies in the same patients performed at 120 kVp (CTDIvol 59). Signal (Hounsfield units [HU]), noise (sd HU), and contrast-to-noise ratio per dose (CNRD) were assessed in multiple cerebral gray and white matter regions of interest. A noise correction factor was used to compensate for scanning at different CTDIvol values. Average gray matter signal at 80 kVp and 120 kVP was 33.9 ± 3.5 HU and 29 ± 4.6 HU, respectively (p<0.0001); the averages for white matter were 22.5 ± 3.1 HU and 21.6 ± 4.6 HU, respectively (p=0.11). Corrected noise was 3 ± 0.6 and 2.7 ± 0.6, respectively, for gray matter (p=0.0001), and 2.8 ± 0.6 and 2.6 ± 0.5, respectively, for white matter (p=0.00001). The gray-white matter CNRD was 4.0 ± 1.2 at 80 kVp and 2.8 ± 1 at 120 kVp (p<0.00001). Cerebral gray-white matter CNRD is increased by 40% at 80 kVp compared to conventional 120 kVp CT scans. These findings justify further clinical evaluation in the acute stroke setting.

Computer-aided classification of BI-RADS category 3 breast lesions.

PURPOSE: To evaluate a system for computer-aided classification (CAC) of lesions assigned to Breast Imaging Reporting and Data System (BI-RADS) category 3 at conventional mammographic interpretation. MATERIALS AND METHODS: A CAC system was used to analyze 106 cases of lesions (42 malignant) that at blinded retrospective interpretation were assigned to BI-RADS category 3 by at least two of four radiologists. The CAC system automatically extracted from the digitized mammograms quantitative features that characterized the lesions. The system then used a classification scheme to score the lesions by the likelihood of their malignancy on the basis of these features. The classification scheme was trained with 646 pathologically proved cases (323 malignant), and the results were tested with receiver operating characteristic (ROC) analysis by using the jackknife method. Sensitivity, specificity, positive predictive value, and accuracy were calculated. Category 3 lesions were stratified among BI-RADS categories 2-5 according to CAC-assigned lesion score, and this classification was compared with the results of pathologic analysis. RESULTS: Jackknife analysis of CAC results in the training data set yielded a sensitivity of 94%, specificity of 78%, positive predictive value of 81%, and area under the ROC curve of 0.90. Of the 42 malignant lesions that had been classified at conventional interpretation as probably benign, nine were assigned by the CAC system to BI-RADS category 4, and 29 were assigned to category 5. The CAC system correctly upgraded the BI-RADS classification of these 38 lesions (sensitivity, 90%) and incorrectly upgraded the classification of only 20 benign lesions (specificity, 69%). CONCLUSION: The CAC system scored 38 of the 42 malignant lesions initially assigned to BI-RADS category 3 as BI-RADS category 4 or 5, and thus correctly upgraded the category in 90% of these lesions.

Can the size of microcalcifications predict malignancy of clusters at mammography?

RATIONALE AND OBJECTIVES: The purpose of this study was to determine whether the size of mammographically detected microcalcifications is predictive of malignancy. MATERIALS AND METHODS: Two hundred sixty mammograms showing clustered microcalcifications with proven diagnoses (160 malignant, 100 benign) were respectively reviewed by experienced mammographers. Lesions that were obviously benign in appearance were excluded from the study. A computer-aided diagnosis system digitized the lesions at 600 dpi, and the microcalcifications on the digital image were interactively defined by mammographers. Subsequently, three quantitative features that reflected the size of the microcalcifications-length, area, and brightness-were automatically extracted by the system. For each feature, the standard average of values obtained for individual calcifications within the cluster and the average with emphasis on extreme values (E) obtained in a single cluster were analyzed and matched with pathologic results. RESULTS: In the malignant group of cases, the mean values of the standard average length and area were significantly higher (P < .0001) than the mean values in the benign group. Distribution analysis demonstrated that an average length of more than 0.41 mm was associated with malignant lesions 77% of the time, while an average length of less than 0.41 mm was associated with benign lesions 71% of the time. The mean of the average length (E) and area (E) of microcalcifications within the cluster demonstrated an even higher discriminative power when compared with the standard average length and area. The average brightness, on the other hand, showed only a low discriminative power. CONCLUSION: Digital computerized analysis of mammographically detected calcifications demonstrated that the average length and area of the calcifications in benign clusters were significantly smaller than those in malignant clusters.