Performance of a Previously Validated CT Colonography Computer-Aided Detection System in a New Patient Population

Abstract

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.

CT colonography: computer-assisted detection of colorectal cancer

“…the algorithm was developed and validated using the same data. This approach presents a relatively weak challenge to the software and external validation paradigms that are generally preferred [17,21]. Taylor and colleagues [22] used such an external validation paradigm to investigate CAD performance in 24 patients with early (T1) colorectal cancers that were morphologically flat, finding that CAD identified 20 (83.3%) of the tumours.…”

“…Our methodology has been used extensively in prior studies of CAD [12,17,21] but can only indirectly assess the impact that CAD might have on radiologist interpretation. We identified three cancers that had been missed during the original clinical interpretation but which were detected by CAD subsequently.…”

“…It has been suggested that computer-aided detection (CAD) may enhance reader performance and perhaps also diminish the learning curve for CTC [11,12]. Recent studies confirm that reader sensitivity is increased when interpretation is supplemented by CAD [13][14][15][16].Most claims for the potential benefits of CAD have been made in the context of colorectal cancer screening [12,17], which targets both early cancer and its precursor, the adenomatous polyp. In asymptomatic individuals, the prevalence of adenomas vastly exceeds that of early cancer and so CAD systems have naturally been optimised for polyp detection.…”

“…Most claims for the potential benefits of CAD have been made in the context of colorectal cancer screening [12,17], which targets both early cancer and its precursor, the adenomatous polyp. In asymptomatic individuals, the prevalence of adenomas vastly exceeds that of early cancer and so CAD systems have naturally been optimised for polyp detection.…”

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

“…CAD performance increased 5%-10% when higher quality CTC data was tested. 37 In addition, electronic cleansing, 38 logistic regression, 39 massivetraining artificial neural network (MTANN), 40 supine-prone correspondence, 41 and feature-guided analysis 42 have been utilized to improve CAD performance by false positive reduction. While each improvement alone increases sensitivity only a small amount, it is possible to get a clinically useful CAD system with high performance if all of these efforts are combined.…”