CAD False positive

One of the common, noticeable problems with CAD is that it tends to generate a much large number of false-positives than does human readers (Roehrig 2005). False-positives may lead to uimecessary further workups such as polypectomy by colonoscopy. Therefore, knowledge about the pattern of the CAD false-positives is important for dismissing them and for reducing the unnecessary false-positives. 

Studies have shown that most of the false-positives detected by CAD tend to exhibit polyp-like shapes, and the major causes of CAD false-positives are thickened haustral folds and retained stool (PiCKHARDT 2004 Yoshida et al. 2002a Yoshida et al. 2002b), which is similar to the cause of false-positives by human readers (Pickhardt 2004). An example of the breakdown of the false-positive sources is the following Approximately half (45%) of the false-positives are caused by folds or flexural pseudotumors. They consist of sharp folds at the sigmoid colon, folds prominent on the colonic wall, two converging folds, ends of folds in the tortuous colon, and folds in the not-well-distended colon. One fifth (20%) are caused by solid stool, which is often a major source of error for radiologists as well. Approximately 15% are caused by residual materials inside the small bowel and stomach, and 10% are... 

Fortunately, the majority of the CAD false-positives - approximately 80-90% of them - can be dismissed relatively easily based on their characteristic locations and appearance, and thus they are not a productivity hindrance (Okamura et al. 2004 Taylor et al. 2003). For example, a falsepositive detection on a thickened fold can be easily dismissed in a 3D endoluminal view, in which the reader can see the global structure of a fold on which a small bump that CAD points to is located. False-positives due to ileocecal valves and the rectal tube can easily be dismissed based on their anatomic location and shape a semi-automated recognition of ileocecal valves (Summers et al. 2004) and rectal tubes (lORDANESCU and Summers 2004 Suzuki et al. 2006) may make this already easy task even easier (Summers et al. 2004). Solid stool can be difficult false-positives to dismiss however, one may distinguish them from polyps by visual correspondence analysis between prone and supine views this relatively elaborate task can also be facilitated by a computerized correspondence between supine and prone data sets (Nappi et al. 2004c). 

Fig. 27.7.a-d Example of CAD false-positives. a Prominent fold. The tip of the fold in the figure (arrow) appears to he a cap-like structure, and thus it was incorrectly identified hy CAD as a polyp, b Solid stool. This polyp-mimicking stool has a cap-like appearance and a solid internal texture pattern, and thus it was detected incorrectly as a polyp, c Ileocecal valve. The tip of the ileocecal valve often has the cap-like appearance of a polyp and thus can be a cause of false-positives in CAD. d Residual materials inside the small bowel and stomach. Although a majority of the small bowel and stomach is removed in the colon extraction step, a small piece of them may be extracted along with the colon, and thus residual materials in the small bowel and stomach can cause false-positives. (Reprint, with permission, from (Yoshida and Dachman 2005)... 

However, there are types of false-positives, such as solid stool that mimics the shape of polyps and adheres to the colonic wall, vdiich are difficult to differentiate from polyps even for an experienced radiologist. One study has shown that approximately one false-positive per case required more detailed problem solving (Taylor et al. 2003) another study showed that, on average, 23% of the CAD false-positives were erroneously interpreted as polyps hy a reader (Okamura et al. 2004). Moreover, the pattern of the false-positives may differ across different CAD systems. More research is required for establishing how radiologists can remove these false-positives to make a correct final diagnosis more reliably. 

Fig. 11.7a-d. Example of CAD false positives. (Reprint, with permission, from Yoshida and Dachman 2005)... 

Jiang et al. (2001) demonstrated that the detection of a high number of false-positive computer-detected microcalcifications degrades classification performance of the global CAD system for clustered microcaldfications substantially 0iang et al. 2001). Therefore, the aim of the following research activities was to differentiate between dustered and non-... 

In 1995, Zheng et al. (1995c) reported a multistage CAD scheme using a Gaussian band-pass filter and nonlinear threshold operation followed by a multilayer ANN to reduce the number of false-positively detected clusters. 

The polyp candidates thus detected include a large number of false-positive findings. Studies have shown that prominent folds and stool are major sources of false-positives in CAD (Yoshida et al. 2002a Yoshida et al. 2002b). Various methods characterising false-positives have been developed... 

Nevertheless, the above studies appear to indicate that CAD is likely to succeed in detecting polyps with high sensitivity and a low false-positive rate - it appears that the performance of CAD schemes ranges between 70 and 100% by-patient sensitivity for polyps >6 mm, with 2-8 false-positives per patient, and the performance can reach up to 100% by-patient sensitivity with 1.3 false-positives per patient for polyps >5 mm (Nappi and Yoshida 2003). Reported performance of CTC showed that, for human readers, the pooled by-patient sensitivity for polyps > 10 mm and for those 6-9 mm was 85% and 70%, respectively (Mulhall et al. 2005). Comparing this performance with that of CAD, it appears that the performance, especially the sensitivity, of CAD is comparable or even superior to that of human readers. 

Knowing the typical patterns of pitfalls, for example types of false-positives and negatives that frequently occur in CAD, is important for the efficient use of CAD as a detection aid in a clinical setting. The following subsections explain common sources of false-positives and negatives in CAD. 

CAD techniques for CTC have advanced substantially during the last several years. As a result, a fundamental CAD scheme for the detection of polyps has been established, and commercial products are now available. Thus far, CAD shows the potential for detecting polyps and cancers with high sensitivity and with a clinically acceptable low false-positive rate. However, CAD for CTC needs to be improved further for more accurate and reliable detection of polyps and cancers. There are a number of technical challenges that CAD must overcome, and the resulting CAD systems should be evaluated based on large-scale, multicenter, prospective clinical trials. If the assistance in interpretation offered by CAD is shown to improve the diagnostic performance sub-... 

The latest prototype CAD systems yield a clinically acceptable high sensitivity and a low false-positive rate (see Sect. 11.4), and they are becoming integrated into the 3D workstation for CTC examinations and thus into clinical workflow. However, some technical and clinical challenges still remain as open problems for CAD to become a clinical reality. 

To date, most of the CAD schemes developed in academia and in industry comprise of the following four fundamental steps (1) extraction of the colonic wall from the CTC images, (2) detection of polyp candidates in the extracted colon, (3) characterization of false positives, and (4) discrimination between false positives and polyps. A brief descrip-... 

Among the studies published in peer-reviewed journals that describe a full CAD scheme, the CAD scheme developed at the University of Chicago yielded a 95% by-polyp sensitivity, with an average of 1.5 false positives per patient (0.7 false positives per... 

The CAD system at the University Hospital Gast-huisberg achieved an 80% hy-polyp sensitivity, with 8.2 false positives per patient (4.1 false positives per data set), based on 18 patients, with 15 polyps 5 mm in 9 patients (Kiss et al. 2002). In this study, fecal tagging was used for most of the cases. A group at Stanford reported a 100% sensitivity with 7.0 false positives per data set (only the supine data set of each patient was used) based on 8 patients that included a total of 7 polyps >10 mm in 4 patients (Pair et al. 

These studies indicate that CAD is promising in detecting polyps with high sensitivity and a low false-positive rate. It appears that the detection performance can reach up to 100% by-patient sensitivity with 1.3 false positives per patient for polyps >5 mm (Nappi and Yoshida 2003). Generally, however, the performance of CAD systems appears to range between 70 and 100% by-patient sensitivity for... 

Fig. 11.10. Example of false positives in CAD for fecal-tagging CTC. (Courtesy of R Lefere, M.D., Stedelijk Ziekenhuis, Roeselare, Belgium)...

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