Detection For CT Colonography Research Articles

EP.TU.313Colorectal cancer follow up: Can CT Colonography replace Colonoscopy?

Abstract Aims Current guidelines recommend surveillance colonoscopy at one year following Colorectal Cancer Resection (CCR), yearly CT chest, abdomen and pelvis, CEA, and colonoscopy in 3 years. Previous studies showed no significant difference between CTC and colonoscopy detection rates of colorectal cancer or polyps >6mm. A review of abnormalities detected on surveillance colonoscopies one year following CCR. If the incidence is low CT Colonography (CTC) would be an alternative to colonoscopy and, when performed simultaneously with surveillance CT chest would be cost-effective, and help in the selective use of colonoscopy. Methods A retrospective analysis of one-year surveillance colonoscopies following CCR in 2016 at a health board with three different sites. Normal colonoscopy criteria included: no polyps, no tumour, and no abnormality at the anastomosis. Subtotal colectomy, panproctocolectomies and incomplete colonoscopies were excluded. Results 111 surveillance colonoscopies were performed one-year post CCR. Age range 30-87 years (39 patients were above 75). Ninety scopes were normal (81.1%). Eight identified only hyperplastic polyps (7.2%); indirectly making over 88% of surveillance colonoscopies unremarkable. Detected abnormalities: nine low-grade adenomas (8.1%), one anastomotic recurrence (0.9%), and only three new cancers (2.7%). There were no complications related to the procedure. Each colonoscopy costs £996 at this health board, CTC is significantly cheaper. Conclusion New cancer or recurrence post-CCR detected at one-year colonoscopy is very low; therefore, CTC would be ideal alternative surveillance. Adding it to CT Chest would significantly reduce the number of hospital attendances per patient, which is more cost-effective and reduces risk.

Improved Sensitivity and Reader Confidence in CT Colonography Using Dual-Layer Spectral CT: A Phantom Study.

Background Limited cathartic preparations for CT colonography with fecal tagging can improve patient comfort but may result in nondiagnostic examinations from poorly tagged stool. Dual-energy CT may overcome this limitation by improving the conspicuity of the contrast agent, but more data are needed. Purpose To investigate whether dual-energy CT improves polyp detection in CT colonography compared with conventional CT at different fecal tagging levels in vitro. Materials and Methods In this HIPAA-compliant study, between December 2017 and August 2019, a colon phantom 30 cm in diameter containing 60 polyps of different shapes (spherical, ellipsoid, flat) and size groups (5-9 mm, 11-15 mm) was constructed and serially filled with simulated feces tagged with four different iodine concentrations (1.26, 2.45, 4.88, and 21.00 mg of iodine per milliliter), then it was scanned with dual-energy CT with and without an outer fat ring to simulate large body size (total diameter, 42 cm). Two readers independently reviewed conventional 120-kVp CT and 40-keV monoenergetic dual-energy CT images to record the presence of polyps and confidence (three-point scale.) Generalized estimating equations were used for sensitivity comparisons between conventional CT and dual-energy CT, and a Wilcoxon signed-rank test was used for reader confidence. Results Dual-energy CT had higher overall sensitivity for polyp detection than conventional CT (58.8%; 95% confidence interval [CI]: 49.7%, 67.3%; 564 of 960 polyps vs 42.1%; 95% CI: 32.1%, 52.8%; 404 of 960 polyps; P < .001), including with the fat ring (48% and 31%, P < .001). Reader confidence improved with dual-energy CT compared with conventional images on all tagging levels (P < .001). Interrater agreement was substantial (κ = 0.74; 95% CI: 0.70, 0.77). Conclusion Compared with conventional 120-kVp CT, dual-energy CT improved polyp detection and reader confidence in a dedicated dual-energy CT colonography phantom, especially with suboptimal fecal tagging. © RSNA, 2020.

CT Colonography Performance for the Detection of Polyps and Cancer in Adults ≥ 65 Years Old: Systematic Review and Meta-Analysis.

We sought to perform a systematic review and meta-analysis of published CT colonography (CTC) studies assessing lesion detection in senior-age (≥ 65 years old) cohorts. We conducted a systematic review of CTC studies published between January 1994 and August 2017 containing data on senior-age adults. The primary endpoint was the CTC-positive rate for large colorectal polyps (≥ 10 mm) and masses. Secondary endpoints included lesions ≥ 6 mm, proven advanced neoplasia, and colorectal cancer (CRC). Study authors were contacted for additional missing data. Random-effects and meta-regression analyses were used to generate pooled estimates and explore contributing factors. A total of 34 studies with 41,680 (18,798 senior-age) subjects were included. Pooled CTC-positive rates among senior-age patients using the 10-mm size threshold were significantly lower in cohorts of patients with no symptoms (8.2%; 95% CI, 6.0-11.1%) compared with cohorts of patients with positive fecal occult blood tests (32.8%; 95% CI, 15.4-56.7%) and other symptoms of CRC (14.0%; 95% CI, 12.0-16.1%). However, a uniformly high positive predictive value for advanced neoplasia at colonoscopy was seen for all senior-age cohorts using a 10-mm threshold (84.3%; 95% CI, 79.3-88.3%; I2 = 0.0) where such data were available. CTC sensitivity for CRC was 93.0% (95% CI, 89.0-95.0%) in senior-age patients compared with 92.0% (95% CI, 84.0-93.0%) in younger patients. Overall, CTC detection rates were higher in senior-age adults compared with younger adults. On average, one in every 12 senior-age adults without symptoms of CRC who underwent screening would be referred to colonoscopy using the 10-mm threshold, with a high yield for advanced neoplasia and high sensitivity for cancer detection. As expected, CTC-positive rates were higher in cohorts of patients 65 years old or older with symptoms of CRC. These results should help inform the Centers for Medicare & Medicaid Services regarding coverage determination of CTC screening for Medicare beneficiaries.

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

Computer-aided detection for CT colonography

CT colonography (CTC) is a non-invasive screening technique for the detection of colorectal polyps, as an alternative to optical colonoscopy in clinical practice. Computer-aided detection (CAD) for CTC refers to a scheme which automatically detects colorectal polyps and masses in CT images of the colon. It has the potential to increase radiologists’ detection performance and greatly shorten the detection time. Over the years, technical developments have advanced CAD for CTC substantially. In this paper, key techniques used in CAD for polyp detection are reviewed. Illustrations about the performance of existing CAD schemes show their relatively high sensitivity and low false positive rate. However, these CAD schemes are still suffering from technical or clinical problems. Some existing challenges faced by CAD are also pointed out at the end of this paper.

Use of CT colonography in the English Bowel Cancer Screening Programme

ObjectiveTo examine use of CT colonography (CTC) in the English Bowel Cancer Screening Programme (BCSP) and investigate detection rates.DesignRetrospective analysis of routinely coded BCSP data. Guaiac faecal occult blood test...

ROC operating point selection for classification of imbalanced data with application to computer-aided polyp detection in CT colonography

Computer-aided detection and diagnosis (CAD) of colonic polyps always faces the challenge of classifying imbalanced data. In this paper, three new operating point selection strategies based on receiver operating characteristic curve are proposed to address the problem. Classification on imbalanced data performs inferiorly because of a major reason that the best differentiation threshold shifts due to the degree of data imbalance. To address this decision threshold shifting issue, three operating point selection strategies, i.e., shortest distance, harmonic mean and anti-harmonic mean, are proposed and their performances are investigated. Experiments were conducted on a class-imbalanced database, which contains 64 polyps in 786 polyp candidates. Support vector machine (SVM) and random forests (RFs) were employed as basic classifiers. Two imbalanced data correcting techniques, i.e., cost-sensitive learning and training data down sampling, were applied to SVM and RFs, and their performances were compared with the proposed strategies. Comparing to the original thresholding method, i.e., 0.488 sensitivity and 0.986 specificity for RFs and 0.526 sensitivity and 0.977 specificity for SVM, our strategies achieved more balanced results, which are around 0.89 sensitivity and 0.92 specificity for RFs and 0.88 sensitivity and 0.90 specificity for SVM. Meanwhile, their performance remained at the same level regardless of whether other correcting methods are used. Based on the above experiments, the gain of our proposed strategies is noticeable: the sensitivity improved from 0.5 to around 0.88 for RFs and 0.89 for SVM while remaining a relatively high level of specificity, i.e., 0.92 for RFs and 0.90 for SVM. The performance of our proposed strategies was adaptive and robust with different levels of imbalanced data. This indicates a feasible solution to the shifting problem for favorable sensitivity and specificity in CAD of polyps from imbalanced data.

Detection of flat colorectal polyps at screening CT colonography in comparison with conventional polypoid lesions

Although the screening of small, flat polyps is clinically important, the role of CT colonography (CTC) screening in their detection has not been thoroughly investigated. To evaluate the detection capability and usefulness of CTC in the screening of flat and polypoid lesions by comparing CTC with optic colonoscopy findings as the gold standard. We evaluated the CTC detection capability for flat colorectal polyps with a flat surface and a height not exceeding 3 mm (n = 42) by comparing to conventional polypoid lesions (n = 418) according to the polyp diameter. Four types of reconstruction images including multiplanar reconstruction, volume rendering, virtual gross pathology, and virtual endoscopic images were used for visual analysis. We compared the abilities of the four reconstructions for polyp visualization. Detection sensitivity for flat polyps was 31.3%, 44.4%, and 87.5% for lesions measuring 2-3 mm, 4-5 mm, and ≥6 mm, respectively; the corresponding sensitivity for polypoid lesions was 47.6%, 79.0%, and 91.7%. The overall sensitivity for flat lesions (47.6%) was significantly lower than polypoid lesions (64.1%). Virtual endoscopic imaging showed best visualization among the four reconstructions. Colon cancers were detected in eight patients by optic colonoscopy, and CTC detected colon cancers in all eight patients. CTC using 64-row multidetector CT is useful for colon cancer screening to detect colorectal polyps while the detection of small, flat lesions is still challenging.

CT colonography for detection and characterisation of synchronous proximal colonic lesions in patients with stenosing colorectal cancer

ObjectiveTo investigate CT colonography (CTC) performance for detecting and characterising synchronous lesions proximal to a stenosing colorectal cancer and to suggest patient management strategies according to the CTC findings.Methods411 consecutive...

Improved computer‐aided detection of small polyps in CT colonography using interpolation for curvature estimationa)

Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation's effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC.

From Point to Local Neighborhood: Polyp Detection in CT Colonography Using Geodesic Ring Neighborhoods

Existing polyp detection methods rely heavily on curvature-based characteristics to differentiate between lesions. These assume that the discrete triangulated surface mesh or volume closely approximates a smooth continuous surface. However, this is often not the case and because curvature is computed as a local feature and a second-order differential quantity, the presence of noise significantly affects its estimation. For this reason, a more global feature is required to provide an accurate description of the surface at hand. In this paper, a novel method incorporating a local neighborhood around the centroid of a surface patch is proposed. This is done using geodesic rings which accumulate curvature information in a neighborhood around this centroid. This geodesic-ring neighborhood approximates a single smooth, continuous surface upon which curvature and orientation estimation methods can be applied. A new global shape index, S is also introduced and computed. These curvature and orientation values will be used to classify the surface as either a bulbous polyp, ridge-like fold or semiplanar structure. Experimental results show that this method is promising (100% sensitivity, 100% specificity for lesions > 10 mm) for distinguishing between bulbous polyps, folds and planar-like structures in the colon.

Scale‐based scatter correction for computer‐aided polyp detection in CT colonography

CT colonography (CTC) is a feasible and minimally invasive method for the detection of colorectal polyps and cancer screening. Computer-aided detection (CAD) of polyps can improve consistency and sensitivity of virtual colonoscopy interpretation and reduce interpretation burden. However, high-density orally administered contrast agents have scatter effects on neighboring tissues. The scattering manifests itself as an artificial elevation in the observed CT attenuation values of the neighboring tissues. This pseudoenhancement phenomenon presents a problem for the application of computer-aided polyp detection, especially when polyps are submerged in the contrast agents. The authors have developed a scale-based correction method that minimizes scatter effects in CTC data by subtraction of the estimated scatter components from observed CT attenuations. By bringing a locally adaptive structure, object scale, into the correction framework, the region of neighboring tissues affected by contrast agents is automatically specified and adaptively changed in different parts of the image. The method was developed as one preprocessing step in the authors' CAD system and was tested by using leave-one-patient-out evaluation on 56 clinical CTC scans (supine or prone) from 28 patients. There were 50 colonoscopy-confirmed polyps measuring 6-9 mm. Visual evaluation indicated that the method reduced CT attenuation of pseudoenhanced polyps to the usual polyp Hounsfield unit range without affecting luminal air regions. For polyps submerged in contrast agents, the sensitivity of CAD with correction is increased 24% at a rate of ten false-positive detections per scan. For all polyps within 6-9 mm, the sensitivity of the authors' CAD with scatter correction is increased 8% at a rate of ten false-positive detections per scan. The authors' results indicated that CAD with this correction method as a preprocessing step can yield a high sensitivity and a relatively low FP rate in CTC.

HDF: Heat diffusion fields for polyp detection in CT colonography

Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of cancer. We propose to use heat diffusion patterns for polyp detection and identification in CTC. The underlying idea of the proposed method is to utilize the heat diffusion process, which is closely related to the diffusion filtering, to generate a vector field (HDF) that is correlated with the shape of the colon wall. The HDF and a local voxel distribution parameter were used for detection and identification. We evaluated the method on real patient CTC data acquired from eight volunteers. 100% sensitivity is achieved at 0.53 false positives per patient for polyps ⩾ 8 mm and 78% sensitivity is achieved at 0.53 false positives per patient when all polyps ⩾ 5 mm are considered.

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.

Support vector machines committee classification method for computer-aided polyp detection in CT colonography 1

A new classification scheme for the computer-aided detection of colonic polyps in computed tomographic colonography is proposed. The scheme involves an ensemble of support vector machines (SVMs) for classification, a smoothed leave-one-out (SLOO) cross-validation method for obtaining error estimates, and use of a bootstrap aggregation method for training and model selection. Our use of an ensemble of SVM classifiers with bagging (bootstrap aggregation), built on different feature subsets, is intended to improve classification performance compared with single SVMs and reduce the number of false-positive detections. The bootstrap-based model-selection technique is used for tuning SVM parameters. In our first experiment, two independent data sets were used: the first, for feature and model selection, and the second, for testing to evaluate the generalizability of our model. In the second experiment, the test set that contained higher resolution data was used for training and testing (using the SLOO method) to compare SVM committee and single SVM performance. The overall sensitivity on independent test set was 75%, with 1.5 false-positive detections/study, compared with 76%-78% sensitivity and 4.5 false-positive detections/study estimated using the SLOO method on the training set. The sensitivity of the SVM ensemble retrained on the former test set estimated using the SLOO method was 81%, which is 7%-10% greater than the sensitivity of a single SVM. The number of false-positive detections per study was 2.6, a 1.5 times reduction compared with a single SVM. Training an SVM ensemble on one data set and testing it on the independent data has shown that the SVM committee classification method has good generalizability and achieves high sensitivity and a low false-positive rate. The model selection and improved error estimation method are effective for computer-aided polyp detection.

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

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

Computer-aided polyp detection in CT colonography using an ensemble of support vector machines

In this paper, we propose a new classification scheme for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The scheme involves an ensemble of support vector machines (SVMs) for classification, a smoothed leave-one-out (SLOO) cross-validation method for obtaining error estimates, and the use of a bootstrap aggregation method for training and model selection. Our use of an ensemble of SVM classifiers with bagging (bootstrap aggregation), built on different feature subsets, is intended to improve classification performance when compared to single SVMs and to reduce the number of false positive detections. The bagging technique has the effect of a virtual increase in the training set size and, as a consequence, also helps to reduce the bias of error estimates when combined with a leave-one-out cross-validation approach. The bootstrap-based model selection technique is used for tuning the SVM parameters.