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CT Colonography in the Detection of Colorectal Polyps and Cancer: Systematic Review, Meta-Analysis, and Proposed Minimum Data Set for Study Level Reporting¹

¹ From the Department of Specialist Radiology (S.H., S.A.T.), University College Hospital, Euston Rd, London, NW1 2BU, England; Intestinal Imaging Centre (C.I.B.) and Cancer Research UK Colorectal Cancer Unit (W.A.), St Mark's Hospital, Northwick Park, London, England; and Cancer Research UK/NHS Centre for Statistics in Medicine, Old Road Campus, Oxford, England (D.G.A., S.M., J.J.D.). Received February 2, 2005; revision requested April 4; revision received May 23; accepted June 20. Supported by a grant from the European Association of Radiology, administered by the European Society of Gastrointestinal and Abdominal Radiology. Address correspondence to S.H.

View larger version (27K): [in a new window]   Figure 1a. Graphs show per-patient analysis for category 1 polyps (ie, large polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Most individual studies had high sensitivity, and all studies had excellent specificity. The fitted summary ROC curve is close to the top left corner.

View larger version (26K): [in a new window]   Figure 1b. Graphs show per-patient analysis for category 1 polyps (ie, large polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Most individual studies had high sensitivity, and all studies had excellent specificity. The fitted summary ROC curve is close to the top left corner.

View larger version (12K): [in a new window]   Figure 1c. Graphs show per-patient analysis for category 1 polyps (ie, large polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Most individual studies had high sensitivity, and all studies had excellent specificity. The fitted summary ROC curve is close to the top left corner.

View larger version (28K): [in a new window]   Figure 2a. Graphs show per-patient analysis for category 2 polyps (ie, medium and large polyps combined). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. All individual studies had good sensitivity, but specificity was variable. Individual studies were more spread out in the ROC space plot when compared with the analysis of category 1 polyps, and the fitted summary ROC curve is further from the top left corner compared with that in Figure 1.

View larger version (27K): [in a new window]   Figure 2b. Graphs show per-patient analysis for category 2 polyps (ie, medium and large polyps combined). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. All individual studies had good sensitivity, but specificity was variable. Individual studies were more spread out in the ROC space plot when compared with the analysis of category 1 polyps, and the fitted summary ROC curve is further from the top left corner compared with that in Figure 1.

View larger version (14K): [in a new window]   Figure 2c. Graphs show per-patient analysis for category 2 polyps (ie, medium and large polyps combined). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. All individual studies had good sensitivity, but specificity was variable. Individual studies were more spread out in the ROC space plot when compared with the analysis of category 1 polyps, and the fitted summary ROC curve is further from the top left corner compared with that in Figure 1.

View larger version (34K): [in a new window]   Figure 3a. Graphs show per-patient analysis for category 3 polyps (ie, all polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Studies were heterogeneous in both average sensitivity and average specificity, as well as overall performance (as indicated by the ROC plot). This heterogeneity precluded a meaningful summary ROC plot.

View larger version (32K): [in a new window]   Figure 3b. Graphs show per-patient analysis for category 3 polyps (ie, all polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Studies were heterogeneous in both average sensitivity and average specificity, as well as overall performance (as indicated by the ROC plot). This heterogeneity precluded a meaningful summary ROC plot.

View larger version (12K): [in a new window]   Figure 3c. Graphs show per-patient analysis for category 3 polyps (ie, all polyps). (a) Forest plot of sensitivity. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (b) Forest plot of specificity. (c) ROC plot of sensitivity versus 1 minus specificity. Studies were heterogeneous in both average sensitivity and average specificity, as well as overall performance (as indicated by the ROC plot). This heterogeneity precluded a meaningful summary ROC plot.

View larger version (52K): [in a new window]   Figure 4a. Graphs show per-polyp analysis. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (a) Forest plot of sensitivity for category 1 polyps (ie, large polyps). (b) Forest plot of sensitivity for category 2 polyps (ie, medium and large polyps combined). (c) Forest plot of sensitivity for category 3 polyps (ie, all polyps). These data show how the performance of CT colonography deteriorates for smaller polyps. Again, we did not pool data for all polyps because of the large amount of heterogeneity observed.

View larger version (58K): [in a new window]   Figure 4b. Graphs show per-polyp analysis. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (a) Forest plot of sensitivity for category 1 polyps (ie, large polyps). (b) Forest plot of sensitivity for category 2 polyps (ie, medium and large polyps combined). (c) Forest plot of sensitivity for category 3 polyps (ie, all polyps). These data show how the performance of CT colonography deteriorates for smaller polyps. Again, we did not pool data for all polyps because of the large amount of heterogeneity observed.

View larger version (42K): [in a new window]   Figure 4c. Graphs show per-polyp analysis. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive. (a) Forest plot of sensitivity for category 1 polyps (ie, large polyps). (b) Forest plot of sensitivity for category 2 polyps (ie, medium and large polyps combined). (c) Forest plot of sensitivity for category 3 polyps (ie, all polyps). These data show how the performance of CT colonography deteriorates for smaller polyps. Again, we did not pool data for all polyps because of the large amount of heterogeneity observed.

View larger version (35K): [in a new window]   Figure 5. Forest plot of sensitivity of CT colonography for detection of cancer. Almost all cancers were detected (96%), but the number per individual study was too small to allow meta-analysis. FN = false-negative, FP = false-positive, TN = true-negative, TP = true-positive.