Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population

Summers, Ronald M.; Yao, Jianhua; Pickhardt, Perry J.; Franaszek, Marek; Bitter, Ingmar; Brickman, Daniel; Krishna, Vamsi; Choi, Jeewook

doi:10.1053/j.gastro.2005.08.054

Cited by 256 publications

(172 citation statements)

References 38 publications

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“…The "stand-alone" performance of a CAD algorithm excludes the radiologist interaction, and merely reports the ability of the software to detect true polyps (sensitivity) and reports the number of false-positive detections per series, which is equivalent to 1-specificity. Since 2002, several researchers have reported the stand-alone sensitivity and specificity of various academic and commercial CAD systems [4,5]. As CAD schemes evolve, performances improve in tandem.…”

Section: Inside the Black Boxmentioning

confidence: 99%

CAD: How it works, how to use it, performance

Regge

Halligan

2013

European Journal of Radiology

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a b s t r a c tComputer-aided diagnosis (CAD) systems are software algorithms designed to assist radiologists (or other practitioners) in solving a diagnostic problem by using a visual prompt (or "CAD mark") to direct the observer towards potential pathology. CT colonography is a recent arrival to CAD, but could represent one of its most fruitful applications in the future. In contrast to other organs, where a variety of different pathologies are equally represented, significant colorectal pathologies other than polyps and cancer are relatively uncommon. As we shall see, this simplifies the diagnostic task for artificial intelligence developers and also for radiologists who, ultimately, must make the final decision.This review aims to present the current state-of-the-art for CAD applied to CT colonography. A brief overview of the technical essentials and of the diagnostic performance of CAD in isolation, is followed by an explanation of how CAD is used in day-to-day practice. The last section will deal with the most controversial issues affecting CAD performance in clinical practice, with a focus on the interaction between human and artificial intelligence.

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Section: Inside the Black Boxmentioning

confidence: 99%

CAD: How it works, how to use it, performance

Regge

Halligan

2013

European Journal of Radiology

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“…Shape-based methods typically rely on various shape features derived from either first order differential geometric quantities [1]; or from second order quantities computed using Hessian matrices [2][3][4]. The shape features take advantage of the fact that polyps tend to have rounded shapes or contain at least local spherical elements; while colonic folds are elongated shapes.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach for False Positive Reduction in CTC CAD

Beddoe

Slabaugh

2011

Virtual Colonoscopy and Abdominal Imaging. Computational Challenges and Clinical Opportunities

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“…Computer-aided detection (CAD) is a promising technique [2][3][4][5] that could be helpful in reducing these falsenegative findings [6,7]. However, even if the CAD performance would be excellent, it does not automatically translate into equivalent reader performance [8,9], i.e., CAD hits can be disregarded by the observer.…”

Section: Introductionmentioning

confidence: 99%

Does a computer-aided detection algorithm in a second read paradigm enhance the performance of experienced computed tomography colonography readers in a population of increased risk?

et al. 2008

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Does a computer-aided detection algorithm in a second read paradigm enhance the performance of experienced computed tomography colonography readers in a population of increased risk?Abstract We prospectively determined whether computer-aided detection (CAD) could improve the performance characteristics of computed tomography colonography (CTC) in a population of increased risk for colorectal cancer. Therefore, we included 170 consecutive patients that underwent both CTC and colonoscopy. All findings ≥6 mm were evaluated at colonoscopy by segmental unblinding. We determined perpatient sensitivity and specificity for polyps ≥6 mm and ≥10 mm without and with computer-aided detection (CAD). The McNemar test was used for comparison the results without and with CAD. Unblinded colonoscopy detected 50 patients with lesions ≥6 mm and 25 patients with lesions ≥10 mm. Sensitivity of CTC without CAD for these size categories was 80% (40/50, 95% CI: 69-81%) and 64% (16/25, 95% CI: 45-83%), respectively. CTC with CAD detected one additional patient with a lesion ≥6 mm and two with a lesion ≥10 mm, resulting in a sensitivity of 82% (41/50, 95% CI: 71-93%) (p=0.50) and 72% (18/25, 95% CI: 54-90%) (p=1.0), respectively. Specificity without CAD for polyps ≥6 mm and ≥10 mm was 84% (101/120, 95% CI: 78-91%) and 94% (136/145, 95% CI: 90-98%), respectively. With CAD, the specificity remained (nearly) unchanged: 83% (99/120, 95% CI: 76-89%) and 94% (136/145, 95% CI: 90-98%), respectively. Thus, although CTC with CAD detected a few more patients than CTC without CAD, it had no statistically significant positive influence on CTC performance.

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Computed Tomographic Virtual Colonoscopy Computer-Aided Polyp Detection in a Screening Population

Cited by 256 publications

References 38 publications

CAD: How it works, how to use it, performance

CAD: How it works, how to use it, performance

A Bayesian Approach for False Positive Reduction in CTC CAD

Does a computer-aided detection algorithm in a second read paradigm enhance the performance of experienced computed tomography colonography readers in a population of increased risk?

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