Computer-Aided Detection of Polyps on Oral Contrast–Enhanced CT Colonography

Automated teniae coli detection and identification on computed tomographic colonography

PURPOSE

Computed tomographic colonography (CTC) is a minimally invasive technique for colonic polyps and cancer screening. Teniae coli are three bands of longitudinal smooth muscle on the colon surface. Teniae coli are important anatomically meaningful landmarks on human colon. In this paper, the authors propose an automatic teniae coli detection method for CT colonography.

METHODS

The original CTC slices are first segmented and reconstructed to a 3D colon surface. Then, the 3D colon surface is unfolded using a reversible projection technique. After that the unfolded colon is projected to a 2D height map. The teniae coli are detected using the height map and then reversely projected back to the 3D colon. Since teniae are located at the junctions where the haustral folds meet, the authors apply 2D Gabor filter banks to extract features of haustral folds. The maximum response of the filter banks is then selected as the feature image. The fold centers are then identified based on local maxima and thresholding on the feature image. Connecting the fold centers yields a path of the folds. Teniae coli are extracted as lines running between the fold paths. The authors used the spatial relationship between ileocecal valve (ICV) and teniae mesocolica (TM) to identify the TM, then the teniae omentalis (TO) and the teniae libera (TL) can be identified subsequently.

RESULTS

The authors tested the proposed method on 47 cases of 37 patients, 10 of the patients with both supine and prone CT scans. The proposed method yielded performance with an average normalized root mean square error (RMSE) ( ± standard deviation [95% confidence interval]) of 4.87% ( ± 2.93%, [4.05% 5.69%]).

CONCLUSIONS

The proposed fully-automated teniae coli detection and identification method is accurate and promising for future clinical applications.

Automated image-based colon cleansing for laxative-free CT colonography computer-aided polyp detection

PURPOSE

To evaluate the performance of a computer-aided detection (CAD) system for detecting colonic polyps at noncathartic computed tomography colonography (CTC) in conjunction with an automated image-based colon cleansing algorithm.

METHODS

An automated colon cleansing algorithm was designed to detect and subtract tagged-stool, accounting for heterogeneity and poor tagging, to be used in conjunction with a colon CAD system. The method is locally adaptive and combines intensity, shape, and texture analysis with probabilistic optimization. CTC data from cathartic-free bowel preparation were acquired for testing and training the parameters. Patients underwent various colonic preparations with barium or Gastroview in divided doses over 48 h before scanning. No laxatives were administered and no dietary modifications were required. Cases were selected from a polyp-enriched cohort and included scans in which at least 90% of the solid stool was visually estimated to be tagged and each colonic segment was distended in either the prone or supine view. The CAD system was run comparatively with and without the stool subtraction algorithm.

RESULTS

The dataset comprised 38 CTC scans from prone and/or supine scans of 19 patients containing 44 polyps larger than 10 mm (22 unique polyps, if matched between prone and supine scans). The results are robust on fine details around folds, thin-stool linings on the colonic wall, near polyps and in large fluid/stool pools. The sensitivity of the CAD system is 70.5% per polyp at a rate of 5.75 false positives/scan without using the stool subtraction module. This detection improved significantly (p = 0.009) after automated colon cleansing on cathartic-free data to 86.4% true positive rate at 5.75 false positives/scan.

CONCLUSIONS

An automated image-based colon cleansing algorithm designed to overcome the challenges of the noncathartic colon significantly improves the sensitivity of colon CAD by approximately 15%.

Distributed human intelligence for colonic polyp classification in computer-aided detection for CT colonography

PURPOSE

To assess the diagnostic performance of distributed human intelligence for the classification of polyp candidates identified with computer-aided detection (CAD) for computed tomographic (CT) colonography.

MATERIALS AND METHODS

This study was approved by the institutional Office of Human Subjects Research. The requirement for informed consent was waived for this HIPAA-compliant study. CT images from 24 patients, each with at least one polyp of 6 mm or larger, were analyzed by using CAD software to identify 268 polyp candidates. Twenty knowledge workers (KWs) from a crowdsourcing platform labeled each polyp candidate as a true or false polyp. Two trials involving 228 KWs were conducted to assess reproducibility. Performance was assessed by comparing the area under the receiver operating characteristic curve (AUC) of KWs with the AUC of CAD for polyp classification.

RESULTS

The detection-level AUC for KWs was 0.845 ± 0.045 (standard error) in trial 1 and 0.855 ± 0.044 in trial 2. These were not significantly different from the AUC for CAD, which was 0.859 ± 0.043. When polyp candidates were stratified by difficulty, KWs performed better than CAD on easy detections; AUCs were 0.951 ± 0.032 in trial 1, 0.966 ± 0.027 in trial 2, and 0.877 ± 0.048 for CAD (P = .039 for trial 2). KWs who participated in both trials showed a significant improvement in performance going from trial 1 to trial 2; AUCs were 0.759 ± 0.052 in trial 1 and 0.839 ± 0.046 in trial 2 (P = .041).

CONCLUSION

The performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.

Sources of false positives in computer-assisted CT colonography

The application of computer-aided detection (CAD) is expected to improve reader sensitivity and to reduce inter-observer variance in computed tomographic (CT) colonography. However, current CAD systems display a large number of false-positive (FP) detections. The reviewing of a large number of FP CAD detections increases interpretation time, and it may also reduce the specificity and/or sensitivity of a computer-assisted reader. Therefore, it is important to be aware of the patterns and pitfalls of FP CAD detections. This pictorial essay reviews common sources of FP CAD detections that have been observed in the literature and in our experiments in computer-assisted CT colonography. Also the recommended computer-assisted reading technique is described.

Colon segmentation for prepless virtual colonoscopy

A novel segmentation framework for a prepless virtual colonoscopy (VC) is presented, which reduces the necessity for colon cleansing before the CT scan. The patient is injected rectally with a water-soluble iodinated contrast medium using manual insufflators and a small rectal catheter. Compared to the air-based contrast medium, this technique can better preserve the color lumen and reduce the partial volume effect. However, the contrast medium, together with the fecal materials and air, makes colon wall segmentation challenging. Our solution makes no assumptions about the shape, size, and location of the fecal material in the colon. This generality allows us to label the fecal material accurately and extract the colon wall reliably. The accuracy of our technique has been verified on 60 human subjects. Compared with current VC technologies, our method is shown to be better in terms of both sensitivity and specificity. Further, in our experiments, the accuracy of the technique was comparable to that of optical colonoscopy results.

Does the amount of tagged stool and fluid significantly affect the radiation exposure in low-dose CT colonography performed with an automatic exposure control?

OBJECTIVE

To determine whether the amount of tagged stool and fluid significantly affects the radiation exposure in low-dose screening CT colonography performed with an automatic tube-current modulation technique.

METHODS

The study included 311 patients. The tagging agent was barium (n = 271) or iodine (n = 40). Correlation was measured between mean volume CT dose index (CTDI (vol)) and the estimated x-ray attenuation of the tagged stool and fluid (ATT). Multiple linear regression analyses were performed to determine the effect of ATT on CTDI (vol ) and the effect of ATT on image noise while adjusting for other variables including abdominal circumference.

RESULTS

CTDI (vol) varied from 0.88 to 2.54 mGy. There was no significant correlation between CTDI (vol) and ATT (p = 0.61). ATT did not significantly affect CTDI (vol) (p = 0.93), while abdominal circumference was the only factor significantly affecting CTDI (vol) (p < 0.001). Image noise ranged from 59.5 to 64.1 HU. The p value for the regression model explaining the noise was 0.38.

CONCLUSION

The amount of stool and fluid tagging does not significantly affect radiation exposure.

Colorectal polyps: stand-alone performance of computer-aided detection in a large asymptomatic screening population

PURPOSE

To evaluate stand-alone performance of computer-aided detection (CAD) for colorectal polyps of 6 mm or larger at computed tomographic (CT) colonography in a large asymptomatic screening cohort.

MATERIALS AND METHODS

In this retrospective, institutional review board-approved, HIPAA-compliant study, a CAD software system was applied to screening CT colonography in 1638 women and 1408 men (mean age, 56.9 years) evaluated at a single medical center between March 2006 and December 2008. All participants underwent cathartic preparation with stool tagging; electronic cleansing was not used. The reference standard consisted of interpretation by experienced radiologists in all cases. This interpretation was further refined for the subset of cases with positive findings by using subsequent colonoscopic or CT colonographic confirmation, as well as retrospective expert localization of polyps with CT colonography. This test set was not involved in training the CAD system. The Fisher exact test was used to evaluate significance; 95% confidence intervals (CIs) were obtained by using the exact method.

RESULTS

Per-patient CAD sensitivities were 93.8% (350 of 373; 95% CI: 90.9%, 96.1%) and 96.5% (137 of 142; 95% CI: 92.0%, 98.8%) at 6- and 10-mm threshold sizes, respectively. Per-polyp CAD sensitivities for all polyps, regardless of histologic features, were 90.1% (547 of 607; 95% CI: 88.0%, 92.8%) and 96.0% (168 of 175; 95% CI: 91.9%, 98.4%) at 6- and 10-mm threshold sizes, respectively; CAD sensitivities for advanced neoplasia and cancer were 97.0% (128 of 132; 95% CI: 92.4%, 99.2%) and 100% (13 of 13; 95% CI: 79.4%, 100%), respectively. The mean and median false-positive rates were 4.7 and 3 per series, respectively (9.4 and 6 per patient). Among 373 patients with a positive finding at CT colonography, CAD marked an additional 15 polyps of 6 mm or larger, including four large polyps, that were missed at the prospective three-dimensional reading by an expert but were found at subsequent colonoscopy.

CONCLUSION

Stand-alone CAD demonstrated excellent performance for polyp detection in a large screening population, with high sensitivity and an acceptable number of false-positive results.

Digital bowel cleansing free colonic polyp detection method for fecal tagging CT colonography

RATIONALE AND OBJECTIVES

Fecal tagging computed tomographic colonography (ftCTC) reduces the discomfort and the inconvenience of patients associated with bowel cleansing procedures before CT scanning. In conventional colonic polyp detection techniques for ftCTC, a digital bowel cleansing (DBC) technique is applied to detect polyps in tagged fecal materials (TFM). However, DBC removes the surface of soft tissues and hampers polyp detection. We developed a colonic polyp detection method for CT colonographic examination that enables the detection of polyps surrounded by air and polyps surrounded by TFM without DBC.

MATERIALS AND METHODS

CT values inside the polyps surrounded by air and polyps surrounded by TFM tend to gradually increase (blob structure) and decrease (inverse-blob structure) from outward to inward, respectively. We developed blob and inverse-blob structure enhancement filters based on the eigenvalues of a Hessian matrix to detect polyps using their intensity characteristic. False-positive elimination is performed using three feature values: volume, maximum value of filter outputs, and standard deviation of CT values inside the polyp candidates.

RESULTS

The proposed method is applied to 104 cases of ftCTC images that include 57 polyps larger than 6 mm in diameter. The sensitivity of the method was 91.2% (52/57) with 11.4 false positives per case.

CONCLUSIONS

The proposed method detects polyps with high sensitivity and 11.4 false positives per case without adverse effects on the DBC.

Computed tomographic colonography

Computed tomographic colonography is a modern technique to evaluate the colon, which may be more appealing to patients than invasive methods. The potential of this test, including current and future developments are presented in this review. The essential steps required to perform a diagnostic quality computed tomographic colonography are discussed as well as different methods of study interpretation. The current status and promising areas of future investigation are also discussed.

Computer-aided detection for CT colonography: update 2007

Computed tomographic colonography (CTC) is an emerging technique for polyp detection in the colon. However, lesion detection can be challenging due to insufficient patient preparation, chosen CT technique or reader imperfection. The primary goal of computer-aided detection (CAD) for CTC is locating possible polyps, and presenting the reader with these polyp candidates. Other goals are sensitivity improvement and reduction of reading time and inter-observer variability. The multistep CAD procedure typically consists of segmentation of the colonic wall (e.g. region growing); selection of intermediate polyp candidates (curvature analysis, sphere fitting, normal analysis, slope density function ...); classification of final candidates for detection and listing suspicious polyps (location, size and volume). Remaining task for the radiologist is the validation or rejection of the polyp candidates. State-of-the-art CAD systems should require minimal or even no user interaction for the extraction of the colonic wall, offer a computation time less than 10-20 min and high sensitivity and specificity for different polyp sizes and shapes, with a low number of false positives. These systems have the potential to increase radiologist's performance and to decrease inter-reader variability. Besides CAD key techniques we also discuss new developments in CAD and describe recent applications facilitating CTC.

Adaptive correction of the pseudo-enhancement of CT attenuation for fecal-tagging CT colonography

In fecal-tagging CT colonography (ftCTC), positive-contrast tagging agents are used for opacifying residual bowel materials to facilitate reliable detection of colorectal lesions. However, tagging agents that have high radiodensity tend to artificially elevate the observed CT attenuation of nearby materials toward that of tagged materials on Hounsfield unit (HU) scale. We developed an image-based adaptive density-correction (ADC) method for minimizing such pseudo-enhancement effect in ftCTC data. After the correction, we can confidently assume that soft-tissue materials and air are represented by their standard CT attenuations, whereas higher CT attenuations indicate tagged materials. The ADC method was optimized by use of an anthropomorphic phantom filled partially with three concentrations of a tagging agent. The effect of ADC on ftCTC was assessed visually and quantitatively by comparison of the accuracy of computer-aided detection (CAD) without and with the use of the ADC method in two different types of clinical ftCTC databases: 20 laxative ftCTC cases with 24 polyps, and 23 reduced-preparation ftCTC cases with 28 polyps. Visual evaluation indicated that ADC minimizes the observed pseudo-enhancement effect. With ADC, the free-response receiver operating characteristic curves indicating CAD performance in polyp detection yielded normalized partial area-under-curve values of 0.91 and 0.80 for the two databases, respectively, with statistically significant improvement over conventional thresholding-based approaches (p<0.05). The results indicate that ADC is a useful method for reducing the pseudo-enhancement effect and for improving CAD performance in CTC.

Computer-aided detection (CAD) using 360 degree virtual dissection: can CAD in a first reviewer paradigm be a reliable substitute for primary 2D or 3D search?

OBJECTIVE

The purpose of this study was to evaluate the feasibility of a new computer-aided detection (CAD) software program as a first reviewer for detecting colorectal polyps when applied to 360 degrees virtual dissection image display.

MATERIALS AND METHODS

Forty-one consecutive patients who underwent imaging without oral contrast material for stool tagging from a teaching file database constituted the patient population for this feasibility study. Using CT colonography equipped with CAD software, reviewers evaluated each possible polyp detected by the software using virtual dissection images combined with axial and 3D endoluminal views and compared the results with optical colonoscopy, the reference standard. Two experienced radiologists blinded to the reference standard findings interpreted the CAD detections to be true or false. The false detections were reviewed and categorized.

RESULTS

Sensitivities for polyps that were 6-9 mm were 78.3% (18/23) and 91.3% (21/23) for reviewers 1 and 2, respectively. For polyps > or = 1 cm, sensitivities were 94.9% (37/39) and 97.4% (38/39) for reviewers 1 and 2, respectively. Per-patient sensitivities for polyps > or = 6 and > or = 10 mm were 94.4% (34/36) and 95.1% (39/41) for reviewer 1 and 97.2% (35/36) and 97.6% (40/41) for reviewer 2, respectively. The average number of false detections per acquisition was 4.28. The average interpretation times were 4 minutes 26 seconds and 5 minutes 38 seconds for reviewers 1 and 2, respectively.

CONCLUSION

Colorectal polyp detection using CT colonography equipped with CAD and virtual dissection as a first reviewer is feasible. Detection rates are similar to colonoscopy. Interobserver variability is low and interpretation times are short. False-positive detections per patient are few in number.

Computer-aided detection of colorectal polyps: can it improve sensitivity of less-experienced readers? Preliminary findings

PURPOSE

To determine whether computer-aided detection (CAD) applied to computed tomographic (CT) colonography can help improve sensitivity of polyp detection by less-experienced radiologist readers, with colonoscopy or consensus used as the reference standard.

MATERIALS AND METHODS

The release of the CT colonographic studies was approved by the individual institutional review boards of each institution. Institutions from the United States were HIPAA compliant. Written informed consent was waived at all institutions. The CT colonographic studies in 30 patients from six institutions were collected; 24 images depicted at least one confirmed polyp 6 mm or larger (39 total polyps) and six depicted no polyps. By using an investigational software package, seven less-experienced readers from two institutions evaluated the CT colonographic images and marked or scored polyps by using a five-point scale before and after CAD. The time needed to interpret the CT colonographic findings without CAD and then to re-evaluate them with CAD was recorded. For each reader, the McNemar test, adjusted for clustered data, was used to compare sensitivities for readers without and with CAD; a Wilcoxon signed-rank test was used to analyze the number of false-positive results per patient.

RESULTS

The average sensitivity of the seven readers for polyp detection was significantly improved with CAD-from 0.810 to 0.908 (P=.0152). The number of false-positive results per patient without and with CAD increased from 0.70 to 0.96 (95% confidence interval for the increase: -0.39, 0.91). The mean total time for the readings was 17 minutes 54 seconds; for interpretation of CT colonographic findings alone, the mean time was 14 minutes 16 seconds; and for review of CAD findings, the mean time was 3 minutes 38 seconds.

CONCLUSION

Results of this feasibility study suggest that CAD for CT colonography significantly improves per-polyp detection for less-experienced readers.

Transparent rendering of intraluminal contrast for 3D polyp visualization at CT colonography

We developed a classifier that permits transparent rendering of both tagging material and air to facilitate interpretation of tagged computed tomographic (CT) colonography. With this technique, a reader can simultaneously appreciate polyps on endoluminal views both covered with tagging material and against air, along with unmodified 2-dimensional CT images. Evaluated with 49 polyps from 26 patients (data from public National Library of Medicine, Health Insurance Portability and Accountability Act compliant), 3 readers were able to determine the presence/absence of polyps in tagged locations with equivalent accuracy compared with polyps in air. This method offers an alternative way to visualize tagged CT colonography.

Screening CT colonography: how I do it

OBJECTIVE

The purpose of this article is to detail an approach to CT colonographic screening that has evolved at one institution.

CONCLUSION

CT colonography is a rapidly advancing technology that has great potential for addressing a deadly but preventable disease-colorectal carcinoma. CT colonography is ideally suited for widespread screening of asymptomatic adults and has become an integral component of the screening efforts at my institution since local third-party coverage was initiated.

Comparative performance of two polyp detection systems on CT colonography

OBJECTIVE

The purpose of our study was to evaluate two current automatic polyp detection systems to determine their sensitivity and false-positive rate in patients who have undergone CT colonography and subsequent endoscopy.

MATERIALS AND METHODS

We evaluated two polyp detection systems--Polyp Enhanced Viewing (PEV) and the Summers computer-aided detection (CAD) system (National Institutes of Health [NIH]) using a unique cohort of CT colonography examinations: 31 examinations with true-positive lesions identified by radiologists and 34 examinations with false-positive lesions incorrectly identified by radiologists. All patients had reference-standard colonoscopy within 7 days of CT. Candidate lesions were compared with the endoscopic reference standard and prospective radiologist interpretation. The sensitivity and false-positive rates were calculated for each system.

RESULTS

The NIH system had a higher sensitivity than the PEV tool for polyps > or = 1 cm (22/23, 96%; 78-99%, 95% CI vs 14/23, 61%; 38-81%, 95% CI; p = 0.008, respectively). There was no significant difference in the detection of medium-sized polyps 6-9 mm in size (8/13 vs 6/13, p = 0.68, respectively). The PEV tool had an average of 1.18 false-positive detections per patient, whereas the NIH tool had an average of 5.20 false-positive detections per patient, with the PEV tool having significantly fewer false-positive detections in both patient groups (p < 0.001).

CONCLUSION

One polyp detection system tended to operate with a higher sensitivity, whereas the other tended to operate with a lower false-positive rate. Prospective trials using polyp detection systems as a primary or secondary means of CT colonography interpretation appear warranted.

Teniae coli-based circumferential localization system for CT colonography: feasibility study

This HIPAA-compliant study, with institutional review board approval and informed patient consent, was conducted to retrospectively develop a teniae coli-based circumferential localization method for guiding virtual colon navigation and colonic polyp registration. Colonic surfaces (n = 72) were depicted at computed tomographic (CT) colonography performed in 36 patients (26 men, 10 women; age range, 47-72 years) in the supine and prone positions. For 70 (97%) colonic surfaces, the tenia omentalis (TO), the most visible of the three teniae coli on a well-distended colonic surface, was manually extracted from the cecum to the descending colon. By virtually dissecting and flattening the colon along the TO, the authors developed a localization system involving 12 grid lines to estimate the circumferential positions of polyps. A sessile polyp would most likely (at 95% confidence level) be found within +/-1.2 grid lines (one grid line equals 1/12 the circumference) with use of the proposed method. By orienting and positioning the virtual cameras with use of the new localization system, synchronized prone and supine navigation was achieved. The teniae coli are extractable landmarks, and the teniae coli-based circumferential localization system helps guide virtual navigation and polyp registration at CT colonography.

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.

CT Colonography (Virtual Colonoscopy): A Practical Approach for Population Screening

CT colonography (CTC), also known as virtual colonoscopy, is a minimally invasive test for the detection of colorectal polyps and masses. At the authors' institution, asymptomatic screening has been the overwhelming indication for CTC referral since local third-party coverage was initiated in April 2004. This practical review details the authors' current approach to CTC screening, which has evolved and matured over time. It discusses the entire spectrum from program set-up through patient disposition following CTC examination. The authors hope this article will provide a roadmap for radiologists who wish to institute a CTC screening program.

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.

Polyethylene glycol solution (PEG) plus contrast medium vs PEG alone preparation for CT colonography and conventional colonoscopy in preoperative colorectal cancer staging

PURPOSE

This study evaluated the usefulness of combined polyethylene glycol solution plus contrast medium bowel preparation (PEG-C preparation) followed by dual-contrast computed tomography enema (DCCTE) and conventional colonoscopy. The main purpose of these examinations is the preoperative staging of already known tumors.

MATERIALS AND METHODS

One hundred patients with colorectal tumors were alternately allocated to either a polyethylene glycol solution preparation (PEG preparation) group (n=50) or a PEG-C preparation group (n=50) before undergoing conventional colonoscopy and computed tomographic (CT) colonography. After conventional colonoscopy, multidetector row CT scans were performed. Air images were reconstructed for both groups; contrast medium images were additionally reconstructed for the PEG-C preparation group. DCCTE images were a composite of air images and contrast medium images without use of dedicated electronic cleansing software. Quality scores (the presence or absence of blind spots of the colon) were compared between the two groups.

RESULTS

Complete tumor images were obtained by DCCTE for all 50 (100%) lesions in the PEG-C preparation group, as compared with only nine of the 50 lesions (18%) in the PEG preparation group (air-contrast CT enema). The overall quality score in the PEG-C preparation group was significantly better than that in the PEG preparation group (P<0.0001).

CONCLUSIONS

DCCTE showed the entire colon without blind spots in nearly all patients in the PEG-C preparation group because the areas under residual fluid were reconstructed as contrast medium images. DCCTE and conventional colonoscopy after PEG-C preparation are feasible and safe procedures that can be used for preoperative evaluation in patients with colorectal cancer.

CT Colonography with Computer-aided Polyp Detection: Volume and Attenuation Thresholds to Reduce False-Positive Findings Owing to the Ileocecal Valve

PURPOSE

To retrospectively identify volume and average attenuation thresholds for differentiating between ileocecal valve (ICV) and polyp at computed tomographic (CT) colonography with computer-aided detection (CAD).

MATERIALS AND METHODS

Informed consent (with consent for future retrospective research) and institutional review board (IRB) approval were obtained for the original prospective study. This retrospective study had IRB approval, as well, and was HIPAA-compliant. A total of 496 patients were selected from a larger screening population. CT colonographic images from 394 patients (227 men, 167 women; mean age, 58.0 years; range, 40-79 years) were used as a training set, and images from 102 patients (76 men, 26 women; mean age, 59.8 years; range, 46-79 years) were used as a test set. A series of 2742 volume and attenuation thresholds, for which segmented findings both larger in volume and lower in average attenuation were labeled as ICVs and remaining findings were labeled polyps, were applied to the training set to determine settings with 100% sensitivity for polyp detection and the highest specificity for ICV detection. The optimal settings were then applied to the test set. Significance was assessed with the Fisher exact test, and 95% confidence intervals (CIs) were computed for sensitivity and specificity.

RESULTS

A total of 386 ICVs and 67 adenomatous polyps from the training set and 102 ICVs and 138 adenomatous polyps from the test set could be segmented with a three-dimensional segmentation algorithm. When supine and prone images were counted individually, 746 nonunique ICVs from the training set and 191 from the test set were segmentable. In the training set, a volume of 600 mm(3) and an attenuation of 36 HU provided 100% sensitivity (67 polyps; 95% CI: 93%, 100%) and the optimal 83% specificity (618 of 746 ICVs; 95% CI: 80%, 85%). When applied to the test set, this combination provided 97% sensitivity (134 of 138 polyps; 95% CI: 92%, 99%) and 84% specificity (160 of 191 ICVs; 95% CI: 78%, 89%). Differences in sensitivity and specificity in the detection of polyps between the sets were not significant.

CONCLUSION

Volume and average CT attenuation thresholds can help differentiate most ICVs from true polyps.

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.

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.

Hybrid segmentation of colon filled with air and opacified fluid for CT colonography

Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.

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.

The role of CT colonography in colorectal cancer screening

Computed tomographic colonography (CTC) is a relatively noninvasive technique for large bowel imaging that has the ability to detect colorectal neoplasia. Already well established as a reliable diagnostic tool in symptomatic patients who are unable to undergo complete colonoscopy, it is now being considered as a viable method for population screening. Advances in technique over the past 10 yr make this an attractive alternative, including reduced bowel preparation and stool tagging, three-dimensional (3D) image reconstruction, computer-aided detection software, and low-radiation dose protocols. CTC may be favored by patients compared to other available screening tests due to the ease of performance and comfort. Although published studies vary in relation to the sensitivity of this test for the detection of polyps, in the best hands a sensitivity of greater than 90% for detection of polyps at least 10 mm in diameter may be obtained. Although not yet endorsed for widespread use by major gastroenterological societies, CTC shows promise as a screening tool.

Automated seed placement for colon segmentation in computed tomography colonography

RATIONALE AND OBJECTIVE

To present an algorithm to automatically locate seeds for colon segmentation in computed tomography colonography (CTC).

MATERIALS AND METHODS

The algorithm automatically locates two points (seeds) inside the colon lumen. Because of their high distention and fixed anatomic position, we focus on the cecum and rectum for automatic seed placement. We use two-dimensional morphological operators that find pockets of colonic air of sufficient size. For the rectum, we search within an inferiorly and centrally located CT slice. For the cecum, we search in a group of CT slices in the middle of the scanned volume on the patient's right side. We applied our automated algorithm to segment the colon in 292 consecutive cases of CTC (146 prone, 146 supine).

RESULTS

After automated seed placement, 83.2% (243 of 292) of the colons were segmented completely and 9.6% (28 of 292) were segmented partially. The unsegmented colon parts were present in datasets where the colon was collapsed in more than one place or because seeds could not be placed in regions filled with fluid. In the remaining 7.2% (21 of 292) of cases, the automatic segmentation leaked outside the colon because of a limitation of the contrast-enhanced fluid detection algorithm.

CONCLUSION

Fully automatic seed placement for colonic segmentation is feasible in the majority of cases without seeding of undesired extracolonic air.