CT Colonography: False-Negative Interpretations

Doshi, Taral; Rusinak, David J.; Halvorsen, Robert A.; Rockey, Don C.; Suzuki, Kenji; Dachman, Abraham H.

doi:10.1148/radiol.2441061122

Cited by 80 publications

(37 citation statements)

References 15 publications

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“…known that most polyps missed at unassisted reading are actually visible on the CT scan when studies are reviewed in retrospect by experienced readers (28,29). In this trial we have shown that observer error is reduced by the addition of CAD.…”

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confidence: 63%

Efficacy of Computer-aided Detection as a Second Reader for 6–9-mm Lesions at CT Colonography: Multicenter Prospective Trial

Regge

Monica²,

Galatola

et al. 2013

Radiology

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efficacy of computer-aided Detection as a second reader for 6-9-mm lesions at cT colonography: Multicenter Prospective Trial 1 Purpose:To assess the effect of computer-aided detection (CAD) as a second reader on the sensitivity and specificity of computed tomographic (CT) colonography in detecting 6-9-mm colorectal cancer (CRC) lesions. Materials and Methods:Individuals with clinical indications for colonoscopy-either for symptoms or as part of participating in a surveillance program or CRC screening-were prospectively enrolled at one of 10 academic centers between July 2007 and May 2009. Institutional review board approval was obtained at each clinical site, and all participants provided written informed consent. All participants underwent CT colonography and colonoscopy on the same day. Experienced readers interpreted the CT colonography images unassisted and then reviewed all colorectal lesion-like structures pinpointed by the CAD algorithm. Segmental unblinding of CT colonoscopy findings at colonoscopy was utilized. The sensitivity and specificity of unassisted and CAD-assisted reading in identifying individuals with 6-9-mm lesions were calculated and compared by means of pairwise analysis. Results:A total of 618 participants (mean age, 57.9 years; 54.5% male) were included in the final analysis. Of these participants, 464 (75.1%) had no lesions 6 mm or larger, and 52 (8.4%) had 6-9-mm lesions. The sensitivity of CT colonography with unassisted reading and that with CADassisted reading in identifying individuals with 6-9-mm lesions was 65.4% (95% confidence interval [CI]: 50.9%, 78.0%) and 76.9% (95% CI: 63.2%, 87.5%; P = .016), respectively. No significant change in specificity was observed: The specificity of CT colonography with unassisted and that with CAD-assisted reading was 91.8% (95% CI: 88.9%, 94.1%) and 90.9% (95% CI: 88.0%, 93.4%; P = .063), respectively. Evaluation of CAD candidates required an additional 1.6 minutes (25th-75th percentile: 1.0 minute to 3.4 minutes). Conclusion:The addition of CAD to reading performed by experienced readers resulted in a significant benefit in the detection of 6-9-mm polyps at CT colonography in this cohort.q RSNA, 2012 Supplemental material: http://radiology.rsna.org/lookup /suppl

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confidence: 63%

Efficacy of Computer-aided Detection as a Second Reader for 6–9-mm Lesions at CT Colonography: Multicenter Prospective Trial

Regge

Monica²,

Galatola

et al. 2013

Radiology

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“…22 The research value of image databases acquired during clinical studies has been realized in other anatomic sites as well, such as CT colonography. 23,24 The development of CAD methods for lung nodule detection, classification, and quantitative assessment can be facilitated and stimulated through the creation of a wellcharacterized repository of thoracic CT scans. A true reference database, however, would provide an even greater benefit to investigators but would require an even greater commitment of time and resources to create the standards and infrastructure required to capture metadata, such as image annotations and pathologic diagnosis.…”

Section: Introductionmentioning

confidence: 99%

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

et al. 2011

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Purpose:The development of computer-aided diagnostic ͑CAD͒ methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography ͑CT͒ scans. The Lung Image Database Consortium ͑LIDC͒ and Image Database Resource Initiative ͑IDRI͒ completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute ͑NCI͒, further advanced by the Foundation for the National Institutes of Health ͑FNIH͒, and accompanied by the Food and Drug Administration ͑FDA͒ through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. Methods: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ͑"noduleՆ 3 mm," "noduleϽ 3 mm," and "non-noduleՆ 3 mm"͒. In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Results:The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "noduleՆ 3 mm" by at least one radiologist, of which 928 ͑34.7%͒ received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. Conclusions:The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.

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“…Current challenges of CT colonography include variable sensitivity and specificity among readers and long learning curves for new readers (similar to mammography). Most reader errors are avoidable and result from errors of detection (12)(13)(14). Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying possible polyps to be evaluated by the reader (15)(16)(17)(18).…”

Section: Frequency Of Cad False-positive Resultsmentioning

confidence: 99%

CT Colonography with Computer-aided Detection: Recognizing the Causes of False-Positive Reader Results

et al. 2014

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1Computed tomography (CT) colonography is a screening modality used to detect colonic polyps before they progress to colorectal cancer. Computer-aided detection (CAD) is designed to decrease errors of detection by finding and displaying polyp candidates for evaluation by the reader. CT colonography CAD false-positive results are common and have numerous causes. The relative frequency of CAD false-positive results and their effect on reader performance on the basis of a 19-reader, 100-case trial shows that the vast majority of CAD false-positive results were dismissed by readers. Many CAD false-positive results are easily disregarded, including those that result from coarse mucosa, reconstruction, peristalsis, motion, streak artifacts, diverticulum, rectal tubes, and lipomas. CAD false-positive results caused by haustral folds, extracolonic candidates, diminutive lesions (<6 mm), anal papillae, internal hemorrhoids, varices, extrinsic compression, and flexural pseudotumors are almost always recognized and disregarded. The ileocecal valve and tagged stool are common sources of CAD falsepositive results associated with reader false-positive results. Nondismissable CAD soft-tissue polyp candidates larger than 6 mm are another common cause of reader false-positive results that may lead to further evaluation with follow-up CT colonography or optical colonoscopy. Strategies for correctly evaluating CAD polyp candidates are important to avoid pitfalls from common sources of CAD false-positive results.

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CT Colonography: False-Negative Interpretations

Abstract: The major contributor to error at CT colonography was observer perceptual error, while observer measurement error played a smaller role.

Cited by 80 publications

References 15 publications

Efficacy of Computer-aided Detection as a Second Reader for 6–9-mm Lesions at CT Colonography: Multicenter Prospective Trial

Efficacy of Computer-aided Detection as a Second Reader for 6–9-mm Lesions at CT Colonography: Multicenter Prospective Trial

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans

CT Colonography with Computer-aided Detection: Recognizing the Causes of False-Positive Reader Results

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