L Honeyfield

Computer-aided detection for CT colonography: incremental benefit of observer training

The purpose of this study was to investigate the incremental effect of focused training on observer performance when using computer-assisted detection (CAD) software to interpret CT colonography (CTC). Six radiologists who were relatively inexperienced with CTC interpretation underwent 1 day of focused training before reading 20 patient datasets with the assistance of CAD software (ColonCAR 1.3, Medicsight PLC). Sensitivity, specificity and interpretation times were determined and compared with previous performance when reading the same datasets but without the benefit of focused training, using the binomial exact test and Wilcoxons signed rank test. Per-polyp sensitivity improved after training by 18% overall (95% confidence interval (CI): 14-24%, p<0.001) and was greatest for polyps of 6-9 mm (26%, 95% CI: 18-34%, p<0.001). Absolute sensitivity was 23% (9-36%), 51% (33-71%) and 74% (44-100%) for polyps of <or=5 mm, 6-9 mm and >or=10 mm, respectively. Specificity fell significantly after focused training (median of 5.5 false positives per 20 datasets (interquartile range (IQR): 4-6) post-training vs median of 2.5 (IQR: 1-5) pre-training, p = 0.03). Interpretation time also increased significantly after training (from a median of 9.3 min (IQR: 9.3-14.5 min) to a median of 17.1 min (IQR: 15.4-19.4 min), p = 0.03). In conclusion, one day of training increases observer polyp sensitivity when using CAD for CTC at the expense of increased reporting time and reduction in specificity.

Influence of Computer-Aided Detection False-Positives on Reader Performance and Diagnostic Confidence for CT Colonography

OBJECTIVE The objective of our study was to investigate whether an increasing number of computer-aided detection (CAD) false-positives decreases reader sensitivity, specificity, and confidence for nonexpert readers of CT colonography (CTC). MATERIALS AND METHODS Fifty CTC data sets (29 men; mean age, 65 years), 25 of which contained 35 polyps > or = 5 mm, were selected in which CAD had 100% polyp sensitivity at two sphericity settings (0 and 75) but differed in the number of false-positives. The data sets were read by five readers twice: once at each sphericity setting. Sensitivity, specificity, report time, and confidence before and after second-read CAD were compared using the paired exact and Students t test, respectively. Receiver operating characteristic (ROC) curves were generated using reader confidence (1-100) in correct case classification (normal or abnormal). RESULTS CAD generated a mean of 42 (range, 3-118) and 15 (range, 1-36) false-positives at a sphericity of 0 and 75, respectively. CAD at both settings increased per-patient sensitivity from 82% to 87% (p = 0.03) and per-polyp sensitivity by 8% and 10% for a sphericity of 0 and 75, respectively (p < 0.001). Specificity decreased from 84% to 79% (sphericity 0 and 75, p = 0.03 and 0.07). There was no difference in sensitivity, specificity, or reader confidence between sphericity settings (p = 1.0, 1.0, 0.11, respectively). The area under the ROC curve was 0.78 (95% CI, 0.70-0.86) and 0.77 (0.68-0.85) for a sphericity of 0 and 75, respectively. CAD added a median of 4.4 minutes (interquartile range [IQR], 2.7-6.5 minutes) and 2.2 minutes (IQR, 1.2-4.0 minutes) for a sphericity of 0 and 75, respectively (p < 0.001). CONCLUSION. CAD has the potential to increase the sensitivity of readers inexperienced with CTC, although specificity may be reduced. An increased number of CAD-generated false-positives does not negate any beneficial effect but does reduce efficiency.

CT colonography: computer-assisted detection of colorectal cancer

OBJECTIVES Computer-aided detection (CAD) for CT colonography (CTC) has been developed to detect benign polyps in asymptomatic patients. We aimed to determine whether such a CAD system can also detect cancer in symptomatic patients. METHODS CTC data from 137 symptomatic patients subsequently proven to have colorectal cancer were analysed by a CAD system at 4 different sphericity settings: 0, 50, 75 and 100. CAD prompts were classified by an observer as either true-positive if overlapping a cancer or false-positive if elsewhere. Colonoscopic data were used to aid matching. RESULTS Of 137 cancers, CAD identified 124 (90.5%), 122 (89.1%), 119 (86.9%) and 102 (74.5%) at a sphericity of 0, 50, 75 and 100, respectively. A substantial proportion of cancers were detected on either the prone or supine acquisition alone. Of 125 patients with prone and supine acquisitions, 39.3%, 38.3%, 43.2% and 50.5% of cancers were detected on a single acquisition at a sphericity of 0, 50, 75 and 100, respectively. CAD detected three cancers missed by radiologists at the original clinical interpretation. False-positive prompts decreased with increasing sphericity value (median 65, 57, 45, 24 per patient at values of 0, 50, 75, 100, respectively) but many patients were poorly prepared. CONCLUSION CAD can detect symptomatic colorectal cancer but must be applied to both prone and supine acquisitions for best performance.