Computer-aided diagnosis scheme for detection of polyps at CT colonography

European Society of Gastrointestinal and Abdominal Radiology (ESGAR): consensus statement on CT colonography

Rapid clinical dissemination of CT colonography (CTC) is occurring in parallel with continued research into technique optimisation and diagnostic performance. A need exists therefore for current guidance as to basic prerequisites for effective clinical implementation. A questionnaire detailing CTC technique, analysis, training and clinical implementation was developed by the European Society of Gastrointestinal and Abdominal Radiology (ESGAR) CTC committee and circulated to all faculty members of previous ESGAR "hands-on" CTC training courses. Responses were collated and a consensus statement produced. Of 27 invited to participate, 24 responded. Reasonable consensus was reached on bowel preparation, colonic distension, patient positioning, use of IV contrast and optimal scan parameters. Both primary 2D and primary 3D analysis were advocated equally, with some evidence that more experienced readers prefer primary 2D. Training was universally recommended, although there was no consensus regarding minimum requirements. CTC was thought superior to barium enema, although recommended for screening only in the presence of validated local experience. There was consensus that polyps 4 mm or less could be ignored assuming agreement from local gastroenterological colleagues. There is increasing consensus amongst European experts as to the current best practice in CTC.

Computer-aided detection of colonic polyps at CT colonography using a Hessian matrix-based algorithm: preliminary study

OBJECTIVE

The purpose of our study was to develop a Hessian matrix-based computer-aided detection (CAD) algorithm for polyp detection on CT colonography (CTC) and to analyze its performance in a high-risk population.

SUBJECTS AND METHODS

The CTC data sets of 35 patients with at least one colonoscopically proven polyp were interpreted with a Hessian matrix-based CAD algorithm, which was designed to depict bloblike structures protruding into the lumen. Our gold standard was a combination of segmental unblinded optical colonoscopy and retrospective unblinded consensus review by two radiologists. Sensitivity of CAD for polyp detection was evaluated on both per-polyp and per-patient bases. The average number of false-positive detections was calculated, and the causes of false-positives and false-negatives were analyzed.

RESULTS

Ninety-four polyps were identified on colonoscopy. Forty-six polyps were smaller than 6 mm and 48 were 6 mm or larger. Seventy-five (79.8%) of these 94 polyps were identified by radiologists in a retrospective review. When colonoscopy was used as a standard of reference, the sensitivity of CAD was 77.1% for polyps 6 mm or larger. For large polyps (> or = 6 mm) that could be identified on retrospective review, the CAD algorithm achieved sensitivities of 92.5% (37/40) and 91.7% (22/24), respectively, on per-polyp and per-patient bases. There were an average of 5.5 false-positive detections per patient and 3.1 false-positive detections per data set for CAD. The two most frequent causes of false-positives on CAD were prominent or converging fold (78/191) and feces (50/191). Of the three polyps 6 mm or larger that were missed by CAD, two had a flat appearance on colonoscopy and the remaining one was located in the narrow area between the rectal tube and the rectal wall.

CONCLUSION

A Hessian matrix-based CAD algorithm for CTC has the potential to depict polyps larger than or equal to 6 mm with high sensitivity and an acceptable false-positive rate.

Computer-aided diagnosis in medical imaging: historical review, current status and future potential

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a "second opinion" and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists' accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.

Symmetric curvature patterns for colonic polyp detection

A novel approach for generating a set of features derived from properties of patterns of curvature is introduced as a part of a computer aided colonic polyp detection system. The resulting sensitivity was 84% with 4.8 false positives per volume on an independent test set of 72 patients (56 polyps). When used in conjunction with other features, it allowed the detection system to reach an overall sensitivity of 94% with a false positive rate of 4.3 per volume.

CAD in CT colonography without and with oral contrast agents: progress and challenges

Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy in the detection of polyps. Computer-aided diagnosis (CAD) for CTC automatically determines the locations of suspicious polyps and masses in CTC and presents them to radiologists, typically as a second opinion. Despite its relatively short history, CAD has become one of the mainstream techniques that could make CTC prime time for screening of colorectal cancer. Rapid technical developments have advanced CAD substantially during the last several years, and a fundamental scheme for the detection of polyps has been established, in which sophisticated 3D image processing, analysis, and display techniques play a pivotal role. The latest CAD systems indicate a clinically acceptable high sensitivity and a low false-positive rate, and observer studies have demonstrated the benefits of these systems in improving radiologists' detection performance. Some technical and clinical challenges, however, remain unresolved before CAD can become a truly useful tool for clinical practice. Also, new challenges are facing CAD as the methods for bowel preparation and image acquisition, such as tagging of fecal residue with oral contrast agents, and interpretation of CTC images evolve. This article reviews the current status and future challenges in CAD for CTC without and with fecal tagging.

Effect of computer-aided detection as a second reader in multidetector-row CT colonography

Our purpose was to assess the effect of computer-aided detection (CAD) on lesion detection as a second reader in computed tomographic colonography, and to compare the influence of CAD on the performance of readers with different levels of expertise. Fifty-two CT colonography patient data-sets (37 patients: 55 endoscopically confirmed polyps > or =0.5 cm, seven cancers; 15 patients: no abnormalities) were retrospectively reviewed by four radiologists (two expert, two nonexpert). After primary data evaluation, a second reading augmented with findings of CAD (polyp-enhanced view, Siemens) was performed. Sensitivities and reading time were calculated for each reader without CAD and supported by CAD findings. The sensitivity of expert readers was 91% each, and of nonexpert readers, 76% and 75%, respectively, for polyp detection. CAD increased the sensitivity of expert readers to 96% (P = 0.25) and 93% (P = 1), and that of nonexpert readers to 91% (P = 0.008) and 95% (P = 0.001), respectively. All four readers diagnosed 100% of cancers, but CAD alone only 43%. CAD increased reading time by 2.1 min (mean). CAD as a second reader significantly improves sensitivity for polyp detection in a high disease prevalence population for nonexpert readers. CAD causes a modest increase in reading time. CAD is of limited value in the detection of cancer.

Pitfalls in Multi–Detector Row CT Colonography: A Systematic Approach

Thin-section multi-detector row computed tomographic (CT) colonography is a powerful tool for the detection and classification of colonic lesions. However, each step in the process of a CT colonographic examination carries the potential for misdiagnosis. Suboptimal patient preparation, CT scanning protocol deficiencies, and perception and interpretation errors can lead to false-positive and false-negative findings, adversely affecting the diagnostic performance of CT colonography. These problems and pitfalls can be overcome with a variety of useful techniques and observations. A relatively clean, dry, and well-distended colon can be achieved with careful patient preparation, thereby avoiding the problem of residual stool and fluid. Knowledge of the morphologic and attenuation characteristics of common colonic lesions and artifacts can help identify bulbous haustral folds, impacted diverticula, an inverted appendiceal stump, or mobile polyps, any of which may pose problems for the radiologist. A combined two-dimensional and three-dimensional imaging approach is recommended for each colonic finding. A thorough knowledge of the various pitfalls and pseudolesions that may be encountered at CT colonography, along with use of dedicated problem-solving techniques, will help the radiologist differentiate between definite colonic lesions and pseudolesions.

Fully automated three-dimensional detection of polyps in fecal-tagging CT colonography

RATIONALE AND OBJECTIVES

The presence of opacified materials presents several technical challenges for automated detection of polyps in fecal-tagging computed tomography colonography (ftCTC), such as pseudo-enhancement and the distortion of the density, size, and shape of the observed lesions. We developed a fully automated computer-aided detection (CAD) scheme that addresses these issues in automated detection of polyps in ftCTC.

MATERIALS AND METHODS

Pseudo-enhancement was minimized by use of an adaptive density correction (ADC) method. The presence of tagging was minimized by use of an adaptive density mapping (ADM) method. We also developed a new method for automated extraction of the colonic wall within air-filled and tagged regions. The ADC and ADM parameters were optimized by use of an anthropomorphic phantom. The CAD scheme was evaluated with 32+32 cases from two types of clinical ftCTC databases. The cases in database I had full cathartic cleansing and 40 polyps > or =6 mm, and the cases in database II had reduced cathartic cleansing and 44 polyps > or =6 mm. The by-polyp detection performance of the CAD scheme was evaluated by use of a leave-one-patient-out method with five features, and the results were compared with those of a conventional CAD scheme by use of free-response receiver operating characteristic curves.

RESULTS

The CAD scheme detected 95% and 86% of the polyps > or =6 mm with 3.6 and 4.2 false positives per scan on average in databases I and II, respectively. For polyps > or =10 mm, the detection sensitivity was 94% in database I (with one missed hyperplastic polyp) and 100% in database II at the same false-positive rate. The detection sensitivity of the new CAD scheme was approximately 20% higher than that of the conventional CAD scheme.

CONCLUSIONS

The results show that the CAD scheme developed in this study resolves the technical challenges introduced by fecal tagging, is applicable to a variety of colon preparation regimens, and provides a performance superior to that of conventional CAD schemes.

Computer-aided detection in CT colonography: initial clinical experience using a prototype system

Computer-aided detection (CAD) algorithms help to detect colonic polyps at CT colonography (CTC). The purpose of this study was to evaluate the accuracy of CAD versus an expert reader in CTC. One hundred forty individuals (67 men, 73 women; mean age, 59 years) underwent screening 64-MDCT colonography after full cathartic bowel cleansing without fecal tagging. One expert reader interpreted supine and prone scans using a 3D workstation with integrated CAD used as "second reader." The system's sensitivity for the detection of polyps, the number of false-positive findings, and its running time were evaluated. Polyps were classified as small (< or =5 mm), medium (6-9 mm), and large (> or =10 mm). A total of 118 polyps (small, 85; medium, 19; large, 14) were found in 56 patients. CAD detected 72 polyps (61%) with an average of 2.2 false-positives. Sensitivity was 51% (43/85) for small, 90% (17/19) for medium, and 86% (12/14) for large polyps. For all polyps, per-patient sensitivity was 89% (50/56) for the radiologist and 73% (41/56) for CAD. For large and medium polyps, per-patient sensitivity was 100% for the radiologist, and 96% for CAD. In conclusion, CAD shows high sensitivity in the detection of clinically significant polyps with acceptable false-positive rates.

Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: Suppression of rectal tubes

One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance of a small polyp. Radiologists can easily recognize and dismiss RT-induced FPs; thus, they may lose their confidence in CAD as an effective tool if the CAD scheme generates such "obvious" FPs due to RTs consistently. In addition, RT-induced FPs may distract radiologists from less common true positives in the rectum. Therefore, removal RT-induced FPs as well as other types of FPs is desirable while maintaining a high sensitivity in the detection of polyps. We developed a three-dimensional (3D) massive-training artificial neural network (MTANN) for distinction between polyps and RTs in 3D CTC volumetric data. The 3D MTANN is a supervised volume-processing technique which is trained with input CTC volumes and the corresponding "teaching" volumes. The teaching volume for a polyp contains a 3D Gaussian distribution, and that for a RT contains zeros for enhancement of polyps and suppression of RTs, respectively. For distinction between polyps and nonpolyps including RTs, a 3D scoring method based on a 3D Gaussian weighting function is applied to the output of the trained 3D MTANN. Our database consisted of CTC examinations of 73 patients, scanned in both supine and prone positions (146 CTC data sets in total), with optical colonoscopy as a reference standard for the presence of polyps. Fifteen patients had 28 polyps, 15 of which were 5-9 mm and 13 were 10-25 mm in size. These CTC cases were subjected to our previously reported CAD scheme that included centerline-based segmentation of the colon, shape-based detection of polyps, and reduction of FPs by use of a Bayesian neural network based on geometric and texture features. Application of this CAD scheme yielded 96.4% (27/28) by-polyp sensitivity with 3.1 (224/73) FPs per patient, among which 20 FPs were caused by RTs. To eliminate the FPs due to RTs and possibly other normal structures, we trained a 3D MTANN with ten representative polyps and ten RTs, and applied the trained 3D MTANN to the above CAD true- and false-positive detections. In the output volumes of the 3D MTANN, polyps were represented by distributions of bright voxels, whereas RTs and other normal structures partly similar to RTs appeared as darker voxels, indicating the ability of the 3D MTANN to suppress RTs as well as other normal structures effectively. Application of the 3D MTANN to the CAD detections showed that the 3D MTANN eliminated all RT-induced 20 FPs, as well as 53 FPs due to other causes, without removal of any true positives. Overall, the 3D MTANN was able to reduce the FP rate of the CAD scheme from 3.1 to 2.1 FPs per patient (33% reduction), while the original by-polyp sensitivity of 96.4% was maintained.

CT colonography: screening in individuals at high risk for colorectal cancer

The use of computed tomographic colonography (CTC) as a screening test for colorectal cancer is being advocated with growing enthusiasm by physicians and the public as stronger evidence of its validity and limited invasiveness emerges from the literature. Because the approach to surveillance of colorectal cancer depends on an individual's degree of risk category, which depends on familial and personal histories, it seems logical that the diagnostic performance and cost efficacy of screening CTC may differ according to the characteristics of the target population. Although CTC seems a valid option in low- to average-risk populations, pending a careful assessment of its cost and estimates of its cost efficacy, there are some important issues that should be addressed when it comes to considering its use in high-risk patients. The expected larger number of induced colonoscopies and higher false-positive rates are likely to have a great influence on CTC costs, but if its implementation causes a dramatic increase in the number of patients willing to undergo screening, thanks to its acceptability, then the cost efficacy ratio may ultimately become competitive with all other screening strategies for colorectal cancer. We strongly feel that large and well-conducted trials are needed to clarify the role of CTC in screening patients at increased risk of developing colorectal cancer.

Computer-assisted detection for CT colonography: external validation

AIM

To externally validate a computer-assisted detection (CAD) system for computed tomography (CT) colonography, using data from a single centre uninvolved with the software development.

MATERIALS AND METHODS

Twenty-five multi-detector CT colonography examinations of patients with validated polyps accumulated at a single centre were examined by two readers who used endoscopic and histopathological data to identify polyp coordinates. A CAD system that had been developed using data from elsewhere, and had not previously encountered the present data, was then applied to the data at sphericity filter settings of 0.75 and 0.50 and identified potential polyps. True-positive, false-negative, and false-positive counts were determined by comparison with the known polyp coordinates.

RESULTS

Twenty-five patients had 57 polyps, median size 6mm (range 1-15mm). Per-patient sensitivity for the CAD system was 96% (24 of 25). The CAD system detected 44 (77%) polyps at sphericity setting 0.75 and 49 (86%) polyps at sphericity 0.50: the additional five polyps detected all measured 5mm or less. Sphericity of 0.75 resulted in a median of 10 (one to 34) easily dismissed false-positive prompts per patient and a median of 4 (zero to 15) that needed three-dimensional rendering before dismissal. This rose to 32 (16 to 99) and 11 (three to 35), respectively, at sphericity 0.5.

CONCLUSIONS

A per-patient sensitivity of 96% was found for the CAD system (in patients with a median polyp diameter of 6mm) using external validation, a more stringent test than either internal cross-validation or temporal validation. Decreasing sphericity increases sensitivity for small polyps at the expense of decreased specificity.

Computer assisted detection software for CT colonography: effect of sphericity filter on performance characteristics for patients with and without fecal tagging

The aim of this study is to investigate the effect of changing sphericity filter values on performance of a computer assisted detection (CAD) system for CT colonography for data with and without fecal tagging. Colonography data from 138 patients with 317 validated polyps were divided into those with (86) and without (52) fecal tagging. Polyp coordinates were established by three observers and datasets analysed subsequently by a proprietary CAD system used at four discrete sphericity filter settings. Prompts were compared with the known coordinates in order to determine sensitivity and specificity. Sensitivity was highest at low sphericity; of 164 polyps 6 mm or more, 144 (87.8%) were detected at sphericity 0.3, and 132 (80.1%) at sphericity 0.9. Of 42 polyps measuring 10 mm or more, 40 (95.2%) were detected at sphericity 0.3, and 36 (85.7%) at sphericity 0.9. There was no significant difference in sensitivity for tagged and un-tagged data but specificity was reduced in tagged data at low sphericity and significantly reduced in untagged data at high sphericity. CAD had a sensitivity of 95.2% for polyps measuring 1 cm or more and 87.8% for polyps 6 mm or more when used at a sphericity setting of 0.3. Higher sphericity settings increased specificity while reducing sensitivity. The bowel preparation used significantly impacts on specificity.

Lines of curvature for polyp detection in virtual colonoscopy

Computer-aided diagnosis (CAD) is a helpful addition to laborious visual inspection for preselection of suspected colonic polyps in virtual colonoscopy. Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. Our work tries to reduce the number of false-positive detections in the preselection of polyp candidates. Polyp surface shape can be characterized and visualized using lines of curvature. In this paper, we describe techniques for generating and rendering lines of curvature on surfaces and we show that these lines can be used as part of a polyp detection approach. We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. The visualization of shaded colonic surfaces can be enhanced by rendering the derived lines of curvature on these surfaces. Features strongly correlated with true-positive detections were calculated on lines of curvature and used for the polyp candidate selection. We studied the performance of these features on 5 data sets that included 331 pre-detected candidates, of which 50 sites were true polyps. The winding angle had a significant discriminating power for true-positive detections, which was demonstrated by a Wilcoxon rank sum test with p < 0.001. The median winding angle and inter-quartile range (IQR) for true polyps were 7.817 and 6.770 - 9.288 compared to 2.954 and 1.995 - 3.749 for false-positive detections.

CT colonography: influence of 3D viewing and polyp candidate features on interpretation with computer-aided detection

PURPOSE

To retrospectively determine if three-dimensional (3D) viewing improves radiologists' accuracy in classifying true-positive (TP) and false-positive (FP) polyp candidates identified with computer-aided detection (CAD) and to determine candidate polyp features that are associated with classification accuracy, with known polyps serving as the reference standard.

MATERIALS AND METHODS

Institutional review board approval and informed consent were obtained; this study was HIPAA compliant. Forty-seven computed tomographic (CT) colonography data sets were obtained in 26 men and 10 women (age range, 42-76 years). Four radiologists classified 705 polyp candidates (53 TP candidates, 652 FP candidates) identified with CAD; initially, only two-dimensional images were used, but these were later supplemented with 3D rendering. Another radiologist unblinded to colonoscopy findings characterized the features of each candidate, assessed colon distention and preparation, and defined the true nature of FP candidates. Receiver operating characteristic curves were used to compare readers' performance, and repeated-measures analysis of variance was used to test features that affect interpretation.

RESULTS

Use of 3D viewing improved classification accuracy for three readers and increased the area under the receiver operating characteristic curve to 0.96-0.97 (P<.001). For TP candidates, maximum polyp width (P=.038), polyp height (P=.019), and preparation (P=.004) significantly affected accuracy. For FP candidates, colonic segment (P=.007), attenuation (P<.001), surface smoothness (P<.001), distention (P=.034), preparation (P<.001), and true nature of candidate lesions (P<.001) significantly affected accuracy.

CONCLUSION

Use of 3D viewing increases reader accuracy in the classification of polyp candidates identified with CAD. Polyp size and examination quality are significantly associated with accuracy.

Computer-assisted reader software versus expert reviewers for polyp detection on CT colonography

OBJECTIVE

The purpose of our study was to assess the sensitivity of computer-assisted reader (CAR) software for polyp detection compared with the performance of expert reviewers.

MATERIALS AND METHODS

A library of colonoscopically validated CT colonography cases were collated and separated into training and test sets according to the time of accrual. Training data sets were annotated in consensus by three expert radiologists who were aware of the colonoscopy report. A subset of 45 training cases containing 100 polyps underwent batch analysis using ColonCAR version 1.2 software to determine the optimum polyp enhancement filter settings for polyp detection. Twenty-five consecutive positive test data sets were subsequently interpreted individually by each expert, who was unaware of the endoscopy report, and before generation of the annotated reference via an unblinded consensus interpretation. ColonCAR version 1.2 software was applied to the test cases, at optimized polyp enhancement filter settings, to determine diagnostic performance. False-positive findings were classified according to importance.

RESULTS

The 25 test cases contained 32 nondiminutive polyps ranging from 6 to 35 mm in diameter. The ColonCAR version 1.2 software identified 26 (81%) of 32 polyps compared with an average sensitivity of 70% for the expert reviewers. Eleven (92%) of 12 polyps > or = 10 mm were detected by ColonCAR version 1.2. All polyps missed by experts 1 (n = 4) and 2 (n = 3) and 12 (86%) of 14 polyps missed by expert 3 were detected by ColonCAR version 1.2. The median number of false-positive highlights per case was 13, of which 91% were easily dismissed.

CONCLUSION

ColonCAR version 1.2 is sensitive for polyp detection, with a clinically acceptable false-positive rate. ColonCAR version 1.2 has a synergistic effect to the reviewer alone, and its standalone performance may exceed even that of experts.

Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy

In this paper, we present a computer-aided detection (CAD) method to extract and use internal features to reduce false positive (FP) rate generated by surface-based measures on the inner colon wall in computed tomographic (CT) colonography. Firstly, a new shape description global curvature, which can provide an overall shape description of the colon wall, is introduced to improve the detection of suspicious patches on the colon wall whose geometrical features are similar to that of the colonic polyps. By a ray-driven edge finder, the volume of each detected patch is extracted as a fitted ellipsoid model. Within the ellipsoid model, CT image density distribution is analyzed. Three types of (geometrical, morphological, and textural) internal features are extracted and applied to eliminate the FPs from the detected patches. The presented CAD method was tested by a total of 153 patient datasets in which 45 patients were found with 61 polyps of sizes 4-30 mm by optical colonoscopy. For a 100% detection sensitivity (on polyps), the presented CAD method had an average FPs of 2.68 per patient dataset and eliminated 93.1% of FPs generated by the surface-based measures. The presented CAD method was also evaluated by different polyp sizes. For polyp sizes of 10-30 mm, the method achieved mean number of FPs per dataset of 2.0 with 100% sensitivity. For polyp sizes of 4-10 mm, the method achieved 3.44 FP per dataset with 100% sensitivity.

Polyp detection with CT colonography: primary 3D endoluminal analysis versus primary 2D transverse analysis with computer-assisted reader software

PURPOSE

To retrospectively compare primary three-dimensional (3D) endoluminal analysis with primary two-dimensional (2D) transverse analysis supplemented by computer-assisted reader (CAR) software for computed tomographic (CT) polyp detection and reader reporting times.

MATERIALS AND METHODS

Ethical permission and patient consent were obtained from all donor institutions for use of CT colonography data sets. Twenty CT colonography data sets from 14 men (median age, 61 years; age range, 52-78 years) with 48 endoscopically proved polyps were selected. Polyp coordinates were documented in consensus by three unblinded radiologists to create a reference standard. Two radiologists read the data sets, which were randomized between primary 3D endoluminal views with 2D problem solving and 2D views supplemented by CAR software. Reading times and diagnostic confidence were documented. The CAR software highlighted possible polyps by superimposing circles on the 2D transverse images. Data sets were reread after 1 month by using the opposing analysis method. Detection rates were compared by using the McNemar test. Reporting times and diagnostic confidence were compared by using the paired t test and Mann-Whitney U test, respectively.

RESULTS

Mean sensitivity values for polyps measuring 1-5, 6-9, and 10 mm or larger were 14%, 53%, and 83%, respectively, for 2D CAR analysis and 16%, 53%, and 67%, respectively, for primary 3D analysis. Overall sensitivity values were 41% for 2D CAR analysis and 39% for primary 3D analysis (P=.77). Reader 1 detected more polyps than reader 2, particularly when using the 3D fly-through method (P=.002). Mean reading times were significantly longer with the 3D method (P=.001). Mean false-positive findings were 1.5 for 2D analysis and 5.5 for 3D analysis. Reader confidence was not significantly different between analysis methods (P=.42).

CONCLUSION

Two-dimensional CAR analysis is quicker and at least matches the sensitivity of primary 3D endoluminal analysis, with fewer false-positive findings.

CT colonography in the detection of colorectal polyps and cancer: systematic review, meta-analysis, and proposed minimum data set for study level reporting

PURPOSE

To assess the methodologic quality of available data in published reports of computed tomographic (CT) colonography by performing systematic review and meta-analysis.

MATERIALS AND METHODS

The MEDLINE database was searched for colonography reports published between 1994 and 2003, without language restriction. The terms colonography, colography, CT colonoscopy, CT pneumocolon, virtual colonoscopy, and virtual endoscopy were used. Studies were selected if the focus was detection of colorectal polyps verified with within-subject reference colonoscopy by using key methodologic criteria based on information presented at the Fourth International Symposium on Virtual Colonoscopy (Boston, Mass). Two reviewers independently abstracted methodologic characteristics. Per-patient and per-polyp detection rates were extracted, and authors were contacted, when necessary. Per-patient sensitivity and specificity were calculated for different lesion size categories, and Forest plots were produced. Meta-analysis of paired sensitivity and specificity was conducted by using a hierarchical model that enabled estimation of summary receiver operating characteristic curves allowing for variation in diagnostic threshold, and the average operating point was calculated. Per-polyp sensitivity was also calculated.

RESULTS

Of 1398 studies considered for inclusion, 24 met our criteria. There were 4181 patients with a study prevalence of abnormality of 15%-72%. Meta-analysis of 2610 patients, 206 of whom had large polyps, showed high per-patient average sensitivity (93%; 95% confidence interval [CI]: 73%, 98%) and specificity (97%; 95% CI: 95%, 99%) for colonography; sensitivity and specificity decreased to 86% (95% CI: 75%, 93%) and 86% (95% CI: 76%, 93%), respectively, when the threshold was lowered to include medium polyps. When polyps of all sizes were included, studies were too heterogeneous in sensitivity (range, 45%-97%) and specificity (range, 26%-97%) to allow meaningful meta-analysis. Of 150 cancers, 144 were detected (sensitivity, 95.9%; 95% CI: 91.4%, 98.5%). Data reporting was frequently incomplete, with no generally accepted format.

CONCLUSION

CT colonography seems sufficiently sensitive and specific in the detection of large and medium polyps; it is especially sensitive in the detection of symptomatic cancer. Studies are poorly reported, however, and the authors propose a minimum data set for study reporting.

Evaluation of contrast-enhanced computed tomographic colonography in detection of local recurrent colorectal cancer

AIM: To evaluate the diagnostic accuracy, sensitivity, specificity of contrast-enhanced computed tomographic colonography in detecting local recurrence of colorectal cancer.

METHODS: From January 2000 to December 2004, 434 patients after potentially curative resection for invasive colorectal cancer were followed up for a period ranging from 20 to 55 mo. Eighty of the four hundred and thirty-four patients showing strong clinical evidence for recurring colorectal cancer during the last follow-up were enrolled in this study. Each patient underwent contrast-enhanced computed tomographic colonography and colonoscopy on the same day. Any lesions, biopsies, identified during the colonoscopic examination, immediate complications and the duration of the procedure were recorded. The results of contrast-enhanced computed tomographic colonography were evaluated by comparing to those of colonoscopy, surgical finding, and clinical follow-up.

RESULTS: Contrast-enhanced computed tomographic colonography had a sensitivity of 100%, a specificity of 83% and an overall accuracy of 94% in detecting local recurrent colorectal cancer.

CONCLUSION: Conventional colonoscopy and contrast-enhanced tomographic colonography can complement each other in detecting local recurrence of colorectal cancer.

CT colonography: where have we been and where are we going?

Over the past decade, computed tomographic (CT) colonography (also known as virtual colonoscopy) has been used to investigate the colon for colorectal neoplasia. Numerous clinical and technical advances have allowed CT colonography to advance slowly from a research tool to a viable option for colorectal cancer screening. However, substantial controversy remains among radiologists, gastroenterologists, and other clinicians with regard to the current role of CT colonography in clinical practice. On the one hand, all agree there is much excitement about a noninvasive imaging examination that can reliably depict clinically important colorectal lesions. However, this is tempered by results from several recent studies that show the sensitivity of CT colonography may not be as great when performed and the images interpreted by radiologists without expertise and training. The potential to miss important lesions exists; moreover, if polyps cannot be differentiated from folds and residual fecal matter, unnecessary colonoscopy will be performed. In this review, current issues will be discussed regarding colon cancer and the established and reimbursed strategies to screen for it and the past, current, and potential future role of CT colonography.

Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population

BACKGROUND & AIMS

The sensitivity of computed tomographic (CT) virtual colonoscopy (CT colonography) for detecting polyps varies widely in recently reported large clinical trials. Our objective was to determine whether a computer program is as sensitive as optical colonoscopy for the detection of adenomatous colonic polyps on CT virtual colonoscopy.

METHODS

The data set was a cohort of 1186 screening patients at 3 medical centers. All patients underwent same-day virtual and optical colonoscopy. Our enhanced gold standard combined segmental unblinded optical colonoscopy and retrospective identification of precise polyp locations. The data were randomized into separate training (n = 394) and test (n = 792) sets for analysis by a computer-aided polyp detection (CAD) program.

RESULTS

For the test set, per-polyp and per-patient sensitivities for CAD were both 89.3% (25/28; 95% confidence interval, 71.8%-97.7%) for detecting retrospectively identifiable adenomatous polyps at least 1 cm in size. The false-positive rate was 2.1 (95% confidence interval, 2.0-2.2) false polyps per patient. Both carcinomas were detected by CAD at a false-positive rate of 0.7 per patient; only 1 of 2 was detected by optical colonoscopy before segmental unblinding. At both 8-mm and 10-mm adenoma size thresholds, the per-patient sensitivities of CAD were not significantly different from those of optical colonoscopy before segmental unblinding.

CONCLUSIONS

The per-patient sensitivity of CT virtual colonoscopy CAD in an asymptomatic screening population is comparable to that of optical colonoscopy for adenomas > or = 8 mm and is generalizable to new CT virtual colonoscopy data.

Virtual colonoscopy: current status and future directions

Virtual colonoscopy (CT colonography) is a technique whereby CT images of the cleansed and distended colon are acquired, ostensibly for detecting colonic neoplasia, although also providing additional diagnostic information from extracolonic organs. This article examines the current status of virtual colonoscopy, reviewing the technical parameters, performance characteristics, and issues surrounding implementation in routine clinical practice. Future directions for virtual colonoscopy are explored, including advances toward prepless examinations and automated interpretation.

Computer-aided detection for virtual colonoscopy

Colon cancer is one of the leading causes of cancer deaths in the developed countries. Most colon cancers can be prevented if precursor colon polyps are detected and removed. Virtual colonoscopy, or CT colonography, has shown promise to be the future screening tool for polyp detection, with a number of studies performed at academic institutions showing high sensitivity and specificity. Two main factors limiting CT colonography in general use are its excessive interpretation time and the variable sensitivity among readers. This article discusses the potential of computer-aided detection to address these problems. We also review the current state of research in this field and the future roles and challenges of CAD for CT colonography.

Current status and future potential of computer-aided diagnosis in medical imaging

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.

Colon imaging: computed tomographic colonography

Computed tomographic colonography (CTC, virtual colonoscopy) is an attractive modality with which to image the colon. Many different techniques are available; moreover, during the last several years, advances in hardware and software have been remarkable. Evidence to this date suggests that CTC has varying sensitivity for detection of large colonic lesions, largely dependent on technique and the method of study. A variety of issues related to CTC are reviewed, including evolution of CTC, sensitivity and specificity of CTC, patient experience, extracolonic lesions, advances in colon preparation, and training. It is clear that CTC has great promise, but also that many questions about its use remain to be answered.

Computed tomography colonography: automated diameter and volume measurement of colonic polyps compared with a manual technique--in vitro study

OBJECTIVE

To investigate inter- and intraobserver agreement of automated measurement of polyp diameter in vitro.

METHODS

Two phantoms ("QRM" and "Whiting") containing simulated polyps of known diameter and volume were scanned using 16-detector row computed tomography. Two observers estimated polyp diameter using 3 methods: software calipers ("manual"), freehand boundary identification ("semiautomatic"), and automated software segmentation ("fully automatic").

RESULTS

Intraobserver 95% limits of agreement for diameter were narrowest for the fully automatic method (QRM span: 0.39 mm, 0.48 mm; Whiting span: 0.24 mm, 0 mm). Manual estimates were approximately 10 times wider (QRM span: 3.57 mm, 3.21 mm; Whiting span: 3.2 mm, 2.02 mm). Volume estimates were narrowest for the fully automatic method (span: 24.2 mm, 24.1 mm vs. 97.9 mm, 102.9 mm for semiautomatic measurement). Interobserver agreement for diameter was narrowest for the fully automatic method (QRM span: 0.12 mm, Whiting span: 0.16 mm), with the manual method approximately 18 times wider (QRM span: 2.87 mm, Whiting span: 2.18 mm).

CONCLUSION

Fully automated measurement of polyp diameter and volume is technically feasible and results in superior inter- and intraobserver agreement.

CAD techniques, challenges, and controversies in computed tomographic colonography

Computer-aided diagnosis (CAD) for computed tomographic colonography (CTC) automatically detects the locations of suspicious polyps and masses on CTC and provides radiologists with a second opinion. CAD has the potential to increase radiologists' diagnostic performance in the detection of polyps and masses and to decrease variability of the diagnostic accuracy among readers without significantly increasing the reading time. Technical developments have advanced CAD substantially during the past several years, and a fundamental scheme for the detection of polyps has been established. The most recent CAD systems based on this scheme produce a clinically acceptable high sensitivity and a low false-positive rate. However, CAD for CTC is still under active development, and the technology needs to be improved further. This report describes the expected benefits, the current fundamental scheme, the key techniques used for detection of polyps and masses on CTC, the current detection performance, as well as the pitfalls, challenges, controversies, and the future of CAD.

Virtual Endoscopic Visualization of the Colon by Shape–Scale Signatures

We developed a new visualization method for virtual endoscopic examination of computed tomographic (CT) colonographic data by use of shape-scale analysis. The method provides each colonic structure of interest with a unique color, thereby facilitating rapid diagnosis of the colon. Two shape features, called the local shape index and curvedness, are used for defining the shape-scale spectrum. When we map the shape index and curvedness values within CT colonographic data to the shape-scale spectrum, specific types of colonic structures are represented by unique characteristic signatures in the spectrum. The characteristic signatures of specific types of lesions can be determined by use of computer-simulated lesions or by use of clinical data sets subjected to a computerized detection scheme. The signatures are used for defining a two-dimensional color map by assignment of a unique color to each signature region. The method was evaluated visually by use of computer-simulated lesions and clinical CT colonographic data sets, as well as by an evaluation of the human observer performance in the detection of polyps without and with the use of the color maps. The results indicate that the coloring of the colon yielded by the shape-scale color maps can be used for differentiating among the chosen colonic structures. Moreover, the results indicate that the use of the shape-scale color maps can improve the performance of radiologists in the detection of polyps in CT colonography.

Overview on research and development of computer-aided diagnostic schemes

Computer-aided diagnosis (CAD) has become a practical clinical approach in diagnostic radiology, although at present only in the area of detection of breast cancer in mammograms. Current research efforts have been focused on detection and classification of images of many different types of lesions in a number of organs, obtained with various imaging modalities. It is likely that the present results of CAD are only at the tip of the iceberg. Although automated computer diagnosis is a concept based on computer algorithms only, CAD is a concept established by taking into account equally the roles of physicians and computers. The effect of CAD on differential diagnosis has already indicated that the performance level is high, and that CAD would be ready for clinical trials and commercialization efforts. The presentation of images similar to those of an unknown case may be useful as a supplemental tool for CAD in the differential diagnosis.

Computer-aided detection (CAD) for CT colonography: a tool to address a growing need

Colorectal cancer is the third most common cancer in both men and women. It is estimated that in 2004, nearly 147,000 cases of colon and rectal cancer will be diagnosed in the USA, and approximately 57,000 people would die from the disease; however, only 44% of the eligible population undergoes any type of colorectal cancer screening. Many reasons have been identified for non-compliance, with key ones being patient comfort, bowel preparation and cost. Virtual colonoscopy derived from computed tomography (CT) images is gaining broader acceptance as a screening method for colorectal neoplasia. Our research suggests that computer-aided detection (CAD) as a second reader has great potential in improving polyp detection. The ColonCAD prototype presented in this paper was developed and tested on cases representative of the variability and quality in true clinical practice. Results of this study with 150 patients demonstrate that: the developed algorithm generalises well: the sensitivity for polyps > or = 6 mm is on average 90%; and the median false positive rate is a manageable 3 per volume.

Computerized detection of colorectal masses in CT colonography based on fuzzy merging and wall-thickening analysis

In recent years, several computer-aided detection (CAD) schemes have been developed for the detection of polyps in CT colonography (CTC). However, few studies have addressed the problem of computerized detection of colorectal masses in CTC. This is mostly because masses are considered to be well visualized by a radiologist because of their size and invasiveness. Nevertheless, the automated detection of masses would naturally complement the automated detection of polyps in CTC and would produce a more comprehensive computer aid to radiologists. Therefore, in this study, we identified some of the problems involved with the computerized detection of masses, and we developed a scheme for the computerized detection of masses that can be integrated into a CAD scheme for the detection of polyps. The performance of the mass detection scheme was evaluated by application to clinical CTC data sets. CTC was performed on 82 patients with helical CT scanners and reconstruction intervals of 1.0-5.0 mm in the supine and prone positions. Fourteen patients (17%) had a total of 14 masses of 30-50 mm, and sixteen patients (20%) had a total of 30 polyps 5-25 mm in diameter. Four patients had both polyps and masses. Fifty-six of the patients (68%) were normal. The CTC data were interpolated linearly to yield isotropic data sets, and the colon was extracted by use of a knowledge-guided segmentation technique. Two methods, fuzzy merging and wall-thickening analysis, were developed for the detection of masses. The fuzzy merging method detected masses with a significant intraluminal component by separating the initial CAD detections of locally cap-like shapes within the colonic wall into mass candidates and polyp candidates. The wall-thickening analysis detected nonintraluminal masses by searching the colonic wall for abnormal thickening. The final regions of the mass candidates were extracted by use of a level set method based on a fast marching algorithm. False-positive (FP) detections were reduced by a quadratic discriminant classifier. The performance of the scheme was evaluated by use of a leave-one-out (round-robin) method with by-patient elimination. All but one of the 14 masses, which was partially cut off from the CTC data set in both supine and prone positions, were detected. The fuzzy merging method detected 11 of the masses, and the wall-thickening analysis detected 3 of the masses including all nonintraluminal masses. In combination, the two methods detected 13 of the 14 masses with 0.21 FPs per patient on average based on the leave-one-out evaluation. Most FPs were generated by extrinsic compression of the colonic wall that would be recognized easily and quickly by a radiologist. The mass detection methods did not affect the result of the polyp detection. The results indicate that the scheme is potentially useful in providing a high-performance CAD scheme for the detection of colorectal neoplasms in CTC.

Automated mass detection in contrast-enhanced CT colonography: an approach based on contrast and volume

The purpose of this feasibility study was to design and test an algorithm for automating mass detection in contrast-enhanced CT colonography (CTC). Five patients with known colorectal masses underwent a pre-surgical contrast-enhanced (120 ml volume 1.6 g iodine/s injection rate, 60 s scan delay) CTC in high spatial resolution (16-slice CT: collimation: 16x0.75 mm, tablefeed: 24 mm/0.5 s, reconstruction increment: 0.5 mm). A CT-density- and volume-based algorithm searched for masses in the colonic wall, which was extracted before by segmenting and dilating the colonic air lumen and subtracting the inner air. A radiologist analyzed the detections and causes of false positives. All masses were detected, and false positives were easy to identify. Combining CT density with volume as a cut-off is a promising approach for automating mass detection that should be further refined and also tested in contrast-enhanced MR colonography. More information under http://www.screening.info.

Computer-Aided diagnosis for CT colonography

CT colonography, or virtual colonoscopy, is a promising alternative screening tool for colon cancer. Computer-aided diagnosis (CAD) for CT colonography has the potential to increase radiologists' diagnostic performance in the detection of polyps and to reduce variability of the diagnostic accuracy among readers. Technical developments have advanced CAD for CT colonography substantially during the last several years. This paper describes the key techniques used for CAD for detection of polyps and masses in CT colonography, the current detection performance, and challenges and the future of CAD.

Reduction of false positives on the rectal tube in computer-aided detection for CT colonography

PURPOSE

To eliminate false-positive (FP) polyp detections on the rectal tube (RT) in CT colonography (CTC) computer-aided detection (CAD).

METHODS

We use a three-stage approach to detect the RT: detect the RT shaft, track the tube to the tip and label all the voxels that belong to the RT. We applied our RT detection algorithm on a CTC dataset consisting of 80 datasets (40 patients scanned in both prone and supine positions). Two different types of RTs were present, characterized by differences in shaft/bulb diameters, wall intensities, and shape of tip.

RESULTS

The algorithm detected 90% of RT shafts and completely tracked 72% of them. We labeled all the voxels belonging to the completely tracked RTs (72%) and in 11 out of 80 (14%) cases the RT voxels were partially labeled. We obtained a 9.2% reduction of the FPs in the initial polyp candidates' population, and a 7.9% reduction of the FPs generated by our CAD system. None of the true-positive detections were mislabeled.

CONCLUSIONS

The algorithm detects the RTs with good accuracy, is robust with respect to the two different types of RT used in our study, and is effective at reducing the number of RT FPs reported by our CAD system.

Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography

We evaluated the effect of our novel technique of feature-guided analysis of polyps on the reduction of false-positive (FP) findings generated by our computer-aided diagnosis (CAD) scheme for the detection of polyps from computed tomography colonographic data sets. The detection performance obtained by use of feature-guided analysis in the segmentation and feature analysis of polyp candidates was compared with that obtained by use of our previously employed fuzzy clustering technique. We also evaluated the effect of a feature called modified gradient concentration (MGC) on the detection performance. A total of 144 data sets, representing prone and supine views of 72 patients that included 14 patients with 21 colorectal polyps 5-25 mm in diameter, were used in the evaluation. At a 100% by-patient (95% by-polyp) detection sensitivity, the FP rate of our CAD scheme with feature-guided analysis based on round-robin evaluation was 1.3 (1.5) FP detections per patient. This corresponds to a 70-75% reduction in the number of FPs obtained by use of fuzzy clustering at the same sensitivity levels. Application of the MGC feature instead of our previously used gradient concentration feature did not improve the detection result. The results indicate that feature-guided analysis is useful for achieving high sensitivity and a low FP rate in our CAD scheme.

Virtual colonoscopy: past, present, and future

Virtual colonoscopy is developing into a practical clinical technique. The issues of the steep learning curve and accuracy of the technique are undergoing advances related to patient preparation, scanning technique, reading methods, and CAD. It is probably the best test for patients with an incomplete colonoscopy or for those patients who cannot undergo colonoscopy. Its precise role in screening average-risk patients for colon cancer remains to be defined by ongoing research and clinical trials.

Colorectal cancer screening: a challenge for magnetic resonance colonography

The high incidence of colorectal carcinoma and the fact that colorectal cancer mostly arises from benign adenomas have led to recommendations for screening programs. The introduction of ultrafast three-dimensional datasets acquired by cross-sectional imaging modalities (computed tomography or magnetic resonance imaging) in combination with new postprocessing modes, known as virtual endoscopy, has led to new discussion on the recommendation of screening tests for colorectal cancer. Published results have indicated a high sensitivity for computed tomographic colonography and magnetic resonance-based colonography. Both techniques currently must be combined with colon cleansing. Three-dimensional data acquisition for magnetic resonance-based colonography is less than 1 minute using three-dimensional gradient-echo sequences. The lack of ionizing radiation, the low risk and discomfort to patients, and new techniques of minimized patient preparation make this magnetic resonance technique an attractive diagnostic procedure for colorectal lesions, with many aspects for use as a screening method.

Automated knowledge-guided segmentation of colonic walls for computerized detection of polyps in CT colonography

PURPOSE

We have developed a novel automated technique for segmenting colonic walls for the application of computer-aided polyp detection in CT colonography. In particular, the technique was designed to minimize the presence of extracolonic components, such as small bowel, in the segmented colon.

METHODS

The segmentation technique combines an improved version of our previously reported anatomy-oriented colon segmentation technique with a colon-based analysis step that performs self-adjusting volume-growing within the colonic lumen. Extracolonic components are eliminated by intersecting of the resulting two segmentations, so that the colonic walls remain in the intersection. The technique was evaluated on 88 CT colonography datasets. The colon segmentations were evaluated subjectively by four radiologists, as well as objectively by performance of an automated polyp detection on the segmentation. For comparison, the tests were also performed for the anatomy-oriented colon segmentation technique.

RESULTS

On average, the technique covered 98% of the visible colonic walls. Approximately 50% of the extracolonic components remaining in the anatomy-oriented segmentation were removed, but 10-15% of the segmentation still contained extracolonic components. The dataset-based false-positive rate of the automated polyp detection was improved by 10% without compromising the 100% case-based sensitivity, and the case-based false-positive rate was improved by 15% over the previous false-positive rate.

CONCLUSIONS

The technique segments practically all of the colonic walls in the region of diagnostic quality with a large reduction in the amount of extracolonic components over our previously used technique. The new segmentation improves the specificity of our computer-aided polyp detection scheme significantly without any degradation in detection sensitivity.