Purpose: To evaluate the stand-alone performance of a computer-aided detection (CAD) algorithm for colorectal polyps in a large heterogeneous CT colonography (CTC) database that included both tagged and untagged datasets.
Methods: Written, informed consent was waived for this institutional review board-approved, HIPAA-compliant retrospective study. CTC datasets from 2063 patients were assigned to training (n = 374) and testing (n = 1689). The test set consisted of 836 untagged and 853 tagged examinations not used for CAD training. Examinations were performed at 15 sites in the United States, Asia, and Europe, using 4- to 64-multidetector-row computed tomography and various acquisition parameters. CAD sensitivities were calculated on a per-patient and per-polyp basis for polyps measuring ≥6 mm. The reference standard was colonoscopy in 1588 (94%) and consensus interpretation by expert radiologists in 101 (6%) patients. Statistical testing employed χ, logistic regression, and Mann-Whitney U tests.
Results: In 383 of 1689 individuals, 564 polyps measuring ≥6 mm were identified by the reference standard (347 polyps: 6-9 mm and 217 polyps: ≥10 mm). Overall, CAD per-patient sensitivity was 89.6% (343/383), with 89.0% (187/210) for untagged and 90.2% (156/173) for tagged datasets (P = 0.72). Overall, per-polyp sensitivity was 86.9% (490/564), with 84.4% (270/320) for untagged and 90.2% (220/244) for tagged examinations (P = 068). The mean false-positive rate per patient was 5.14 (median, 4) in untagged and 4.67 (median, 4) in tagged patient datasets (P = 0.353).
Conclusion: Stand-alone CAD can be applied to both tagged and untagged CTC studies without significant performance differences. Detection rates are comparable to human readers at a relatively low false-positive rate, making CAD a useful tool in clinical practice.