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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 findings positive for polyps, 74 polyps of 6 mm or larger were observed in 65 colonic segments; in 48 patients with findings 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 significantly 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 specificity of readers by 0.025 (P = .05)., Conclusion: Use of CAD resulted in a significant 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 specificity by a small amount., ((c) RSNA, 2010.)

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6-9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p = 7.78 x 10(-5)) for the size category of 6-9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p = 7.95 x 10(-5)) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95% CI [0.75%, 16%], p = 0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists.

Objective: A computer-aided detection (CAD) system with high sensitivity in the detection of adenomatous polyps in varied CT colonography (CTC) data sets increases the utility of CAD in the clinical setting. The purpose of this study was to evaluate the standalone performance of an existing CAD system with a new set of CTC data from screening patients at an institution and geographic location different from those at which the CAD system was trained., Materials and Methods: CTC data were collected from the records of 104 patients undergoing screening for colorectal neoplasia. Most of the patients were at average risk, had CTC findings suggestive of polyps, and underwent colonoscopy. Patients underwent cathartic bowel preparation, were given an oral contrast agent, and underwent imaging in the prone and supine positions. The patients had 86 adenomas confirmed at same-day optical colonoscopy; 47 of these tumors were 10 mm in diameter or larger, and 39 measured 6-9 mm. The CTC data were analyzed with an existing CAD system for colonography that was trained with previously acquired data. In a previous non-polyp-enriched screening cohort, the standalone performance of the CAD system was 93.3% (28/30) sensitivity for adenomatous polyps 10 mm or larger, 51.1% (47/92) sensitivity for adenomas 6-9 mm, and a mean false-positive rate of 8.6 per patient. Sensitivity comparisons were made with findings in the previous study., Results: The CAD system had per-polyp sensitivities of 91.5% (43/47; 95% CI, 78.7-97.2%; p = 1.0) for adenomas 10 mm or larger and 82.1% (32/39; 65.9-91.9%; p = 0.0009) for adenomas 6-9 mm. The per-patient sensitivities were 97.6% (40/41; 85.6-99.9%; p = 0.6) for patients with adenomas 10 mm or larger and 82.4% (28/34; 64.8-92.6%; p = 0.047) for patients with adenomas 6-9 mm. The mean and median false-positive rates were 9.6 +/- 9.6 and 7.0 per patient, respectively. Common reasons for CAD misses (false-negative findings) were the presence of adherent contrast medium, flat adenomas, and adenomas located on or adjacent to normal colonic folds. In a random sample, 72.5% (29/40) of false-positive findings were attributable to folds or residual feces., Conclusion: The CAD system evaluated has a high level of performance in the detection of adenomatous polyps with CTC data from a polyp-enriched cohort different from that used to train the system.