Polyp size measurement at CT colonography: what do we know and what do we need to know?
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ABSTRACT:
SUBMITTER: Summers RM
PROVIDER: S-EPMC2875919 | biostudies-other | 2010 Jun
REPOSITORIES: biostudies-other
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Project description:Purpose To report the prevalence and characteristics of serrated polyps identified in a large, average-risk population undergoing screening computed tomographic (CT) colonography. Materials and Methods This HIPAA-compliant retrospective study was approved by the institutional review board of the University of Wisconsin School of Medicine and Public Health. The need for informed consent was waived. Nine thousand six hundred examinations from 8289 patients were enrolled in a single-institution CT colonography-based screening program (from 2004 to 2011) and were evaluated for the presence of nondiminutive serrated lesions and advanced adenomas. The prevalence and characteristics of these lesions were tabulated. Generalized estimating equation regressions of polyp characteristics that may contribute to visualization of serrated lesions were investigated, including polyp size, location, and morphologic appearance; histologic findings; and presence or absence of contrast material tagging. Results Nondiminutive serrated lesions (?6 mm) were seen at CT colonography-based screening with a prevalence of 3.1% (254 of 8289 patients). Sessile serrated adenomas (SSAs) and traditional serrated adenomas (TSAs) constituted 36.8% (137 of 372) and 4.3% (16 of 372) of serrated lesions, respectively; hyperplastic polyps (HPs) accounted for 58.9% (219 of 372 lesions). SSA and TSA tended to be large (mean size, 10.6 mm and 14.1 mm, respectively), with size categories and polyp subgroups significantly associated (P < .0001). SSA tended to be proximal in location (91.2%, 125 of 137 lesions) and flat in morphologic appearance (39.4%, 54 of 137 lesions) compared with TSA and HP. The presence of high-grade dysplasia in serrated lesions was uncommon when compared with advanced adenomas (one of 372 lesions vs 22 of 395 lesions, respectively; P < .0001). Multivariate analysis showed that contrast material tagging markedly improved serrated polyp detection with an odds ratio of 40.4 (95% confidence interval: 10.1, 161.4). Conclusion Serrated lesions are seen at CT colonography-based screening with a nondiminutive prevalence of 3.1%. These lesions tend to be large, flat, and proximal in location. Adherent contrast material coating on these polyps aids in their detection, despite an often flat morphologic appearance. (©) RSNA, 2016 Online supplemental material is available for this article.
| S-EPMC5510740 | biostudies-literature
Project description:Purpose To investigate the effect of increasing navigation speed on the visual search and decision making during polyp identification for computed tomography (CT) colonography Materials and Methods Institutional review board permission was obtained to use deidentified CT colonography data for this prospective reader study. After obtaining informed consent from the readers, 12 CT colonography fly-through examinations that depicted eight polyps were presented at four different fixed navigation speeds to 23 radiologists. Speeds ranged from 1 cm/sec to 4.5 cm/sec. Gaze position was tracked by using an infrared eye tracker, and readers indicated that they saw a polyp by clicking a mouse. Patterns of searching and decision making by speed were investigated graphically and by multilevel modeling. Results Readers identified polyps correctly in 56 of 77 (72.7%) of viewings at the slowest speed but in only 137 of 225 (60.9%) of viewings at the fastest speed (P = .004). They also identified fewer false-positive features at faster speeds (42 of 115; 36.5%) of videos at slowest speed, 89 of 345 (25.8%) at fastest, P = .02). Gaze location was highly concentrated toward the central quarter of the screen area at faster speeds (mean gaze points at slowest speed vs fastest speed, 86% vs 97%, respectively). Conclusion Faster navigation speed at endoluminal CT colonography led to progressive restriction of visual search patterns. Greater speed also reduced both true-positive and false-positive colorectal polyp identification. © RSNA, 2017 Online supplemental material is available for this article.
| S-EPMC5548448 | biostudies-literature
Project description:PurposeTo 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 methodsThis 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.ResultsThe 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).ConclusionThe performance of distributed human intelligence is not significantly different from that of CAD for colonic polyp classification.
| S-EPMC3285221 | biostudies-literature
Project description:OBJECTIVES:To assess whether electronic cleansing (EC) of tagged residue and different computed tomography (CT) windows influence the size of colorectal polyps in CT colonography (CTC). METHODS:A database of 894 colonoscopy-validated CTC datasets of a low-prevalence cohort was retrospectively reviewed to identify patients with polyps ?6 mm that were entirely submerged in tagged residue. Ten radiologists independently measured the largest diameter of each polyp, two-dimensionally, before and after EC in colon, bone, and soft-tissue-windows, in randomised order. Differences in size and polyp count before and after EC were calculated for size categories ?6 mm and ?10 mm. Statistical testing involved 95% confidence interval, intraclass correlation and mixed-model ANOVA. RESULTS:Thirty-seven patients with 48 polyps were included. Mean polyp size before EC was 9.8 mm in colon, 9.9 mm in bone and 8.2 mm in soft-tissue windows. After EC, the mean polyp size decreased significantly to 9.4 mm in colon, 9.1 mm in bone and 7.1 mm in soft-tissue windows. Compared to unsubtracted colon windows, EC, performed in colon, bone and soft-tissue windows, led to a shift of 6 (12,5%), 10 (20.8%) and 25 (52.1%) polyps ?6 mm into the next smaller size category, thus affecting patient risk stratification. CONCLUSIONS:EC and narrow CT windows significantly reduce the size of polyps submerged in tagged residue. Polyp measurements should be performed in unsubtracted colon windows. KEY POINTS:• EC significantly reduces the size of polyps submerged in tagged residue. • Abdominal CT-window settings significantly underestimate 2D sizes of submerged polyps. • Size reduction in EC is significantly greater in narrow than wide windows. • Underestimation of polyp size due to EC may lead to inadequate treatment. • Polyp measurements should be performed in unsubtracted images using a colon window.
| S-EPMC6182748 | biostudies-literature
Project description: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.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.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.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%.
| S-EPMC3254579 | biostudies-other
Project description:PurposeTo evaluate the ability of additional analysis of computed tomographic (CT) colonography images to provide a comprehensive osteoporosis assessment.Materials and methodsThis Health Insurance Portability and Accountability Act-compliant study was approved by our institutional review board with a waiver of informed consent. Diagnosis of osteoporosis and assessment of fracture risk were compared between biomechanical CT analysis and dual-energy x-ray absorptiometry (DXA) in 136 women (age range, 43-92 years), each of whom underwent CT colonography and DXA within a 6-month period (between January 2008 and April 2010). Blinded to the DXA data, biomechanical CT analysis was retrospectively applied to CT images by using phantomless calibration and finite element analysis to measure bone mineral density and bone strength at the hip and spine. Regression, Bland-Altman, and reclassification analyses and paired t tests were used to compare results.ResultsFor bone mineral density T scores at the femoral neck, biomechanical CT analysis was highly correlated (R(2) = 0.84) with DXA, did not differ from DXA (P = .15, paired t test), and was able to identify osteoporosis (as defined by DXA), with 100% sensitivity in eight of eight patients (95% confidence interval [CI]: 67.6%, 100%) and 98.4% specificity in 126 of 128 patients (95% CI: 94.5%, 99.6%). Considering both the hip and spine, the classification of patients at high risk for fracture by biomechanical CT analysis--those with osteoporosis or "fragile bone strength"--agreed well against classifications for clinical osteoporosis by DXA (T score ?-2.5 at the hip or spine), with 82.8% sensitivity in 24 of 29 patients (95% CI: 65.4%, 92.4%) and 85.7% specificity in 66 of 77 patients (95% CI: 76.2%, 91.8%).ConclusionRetrospective biomechanical CT analysis of CT colonography for colorectal cancer screening provides a comprehensive osteoporosis assessment without requiring changes in imaging protocols.
| S-EPMC4699492 | biostudies-literature
Project description:ObjectivesThe aim of this study was to compare the morphology, radiological stage, conspicuity, and computer-assisted detection (CAD) characteristics of colorectal cancers (CRC) detected by computed tomographic colonography (CTC) in screening and symptomatic populations.MethodsTwo radiologists independently analyzed CTC images from 133 patients diagnosed with CRC in (a) two randomized trials of symptomatic patients (35 patients with 36 tumours) and (b) a screening program using fecal occult blood testing (FOBt; 98 patients with 100 tumours), measuring tumour length, volume, morphology, radiological stage, and subjective conspicuity. A commercial CAD package was applied to both datasets. We compared CTC characteristics between screening and symptomatic populations with multivariable regression.ResultsScreen-detected CRC were significantly smaller (mean 3.0 vs 4.3 cm, p?<?0.001), of lower volume (median 9.1 vs 23.2 cm3, p?<?0.001) and more frequently polypoid (34/100, 34 % vs. 5/36, 13.9 %, p?=?0.02) than symptomatic CRC. They were of earlier stage than symptomatic tumours (OR?=?0.17, 95 %CI 0.07-0.41, p?<?0.001), and were judged as significantly less conspicuous (mean conspicuity 54.1/100 vs. 72.8/100, p?<?0.001). CAD detection was significantly lower for screen-detected (77.4 %; 95 %CI 67.9-84.7 %) than symptomatic CRC (96.9 %; 95 %CI 83.8-99.4 %, p?=?0.02).ConclusionsScreen-detected CRC are significantly smaller, more frequently polypoid, subjectively less conspicuous, and less likely to be identified by CAD than those in symptomatic patients.Key points• Screen-detected colorectal cancers (CRC) are significantly smaller than symptomatic CRC. • Screening cases are significantly less conspicuous to radiologists than symptomatic tumours. • Screen-detected CRC have different morphology compared to symptomatic tumours (more polypoid, fewer annular). • A commercial computer-aided detection (CAD) system was significantly less likely to note screen-detected CRC.
| S-EPMC5101282 | biostudies-literature
Project description:PurposeTo compare the Medicare population cost of colorectal cancer (CRC) screening of average risk individuals by CT colonography (CTC) vs. optical colonoscopy (OC).MethodsThe authors used Medicare claims data, fee schedules, established protocols, and other sources to estimate CTC and OC per-screen costs, including the costs of OC referrals for a subset of CTC patients. They then modeled and compared the Medicare costs of patients who complied with CTC and OC screening recommendations and tested alternative scenarios.ResultsCTC is 29% less expensive than OC for the Medicare population in the base scenario. Although the CTC cost advantage is increased or reduced under alternative scenarios, it is always positive.ConclusionCTC is a cost-effective CRC screening option for the Medicare population and will likely reduce Medicare expenditures for CRC screening.
| S-EPMC4628615 | biostudies-literature
Project description:Purpose. The purpose of this study was to evaluate the technical and diagnostic performance of sub-milliSievert ultralow-dose (ULD) CT colonograpy (CTC) in the detection of colonic and extracolonic lesions. Materials and Methods. CTC with standard dose (SD) and ULD acquisitions of 64 matched patients, half of them with colonic findings, were reconstructed with filtered back projection (FBP), hybrid (HIR) and iterative model reconstruction techniques (IMR). Image noise in six colonic segments, in the left psoas muscle and aorta were measured. Image quality of the left adrenal gland and of the colon in the endoscopic and 2D view was rated on a five point Likert scale by two observers, who also completed the reading of CTC for colonic and extracolonic findings. Results. The mean radiation dose estimate was 4.1 ± 1.4 mSv for SD and 0.86 ± 0.17 mSv for ULD for both positions (p < 0.0001). In ULD-IMR, SD-IMR and SD-HIR, the endoluminal noise was decreased in all colonic segments compared to SD-FBP (p < 0.001). There were 27 small (6-9 mm) and 17 large (≥10 mm) colonic lesions that were classified as sessile polyps (n = 38), flat lesions (n = 3), or as a mass (n = 3). Per patient sensitivity and specificity were 0.82 and 0.93 for ULD-FBP, 0.97 and 0.97 for ULD-HIR, 0.97 and 1.0 for ULD-IMR. Per polyp sensitivity was 0.84 for ULD-FBP, 0.98 for ULD-HIR, 0.98 for ULD-IMR. Significantly less extracolonic findings were detected in ULD-FBP and ULD-HIR, but in the E4 category by C-RADS (potentially important findings), the detection was similar. Conclusion. Both HIR and IMR are suitable for sub-milliSievert ULD CTC without sacrificing diagnostic performance of the study.
| S-EPMC4824919 | biostudies-literature
Project description:ObjectivesTo investigate the differentiation of premalignant from benign colorectal polyps detected by CT colonography using deep learning.MethodsIn this retrospective analysis of an average risk colorectal cancer screening sample, polyps of all size categories and morphologies were manually segmented on supine and prone CT colonography images and classified as premalignant (adenoma) or benign (hyperplastic polyp or regular mucosa) according to histopathology. Two deep learning models SEG and noSEG were trained on 3D CT colonography image subvolumes to predict polyp class, and model SEG was additionally trained with polyp segmentation masks. Diagnostic performance was validated in an independent external multicentre test sample. Predictions were analysed with the visualisation technique Grad-CAM++.ResultsThe training set consisted of 107 colorectal polyps in 63 patients (mean age: 63 ± 8 years, 40 men) comprising 169 polyp segmentations. The external test set included 77 polyps in 59 patients comprising 118 polyp segmentations. Model SEG achieved a ROC-AUC of 0.83 and 80% sensitivity at 69% specificity for differentiating premalignant from benign polyps. Model noSEG yielded a ROC-AUC of 0.75, 80% sensitivity at 44% specificity, and an average Grad-CAM++ heatmap score of ≥ 0.25 in 90% of polyp tissue.ConclusionsIn this proof-of-concept study, deep learning enabled the differentiation of premalignant from benign colorectal polyps detected with CT colonography and the visualisation of image regions important for predictions. The approach did not require polyp segmentation and thus has the potential to facilitate the identification of high-risk polyps as an automated second reader.Key points• Non-invasive deep learning image analysis may differentiate premalignant from benign colorectal polyps found in CT colonography scans. • Deep learning autonomously learned to focus on polyp tissue for predictions without the need for prior polyp segmentation by experts. • Deep learning potentially improves the diagnostic accuracy of CT colonography in colorectal cancer screening by allowing for a more precise selection of patients who would benefit from endoscopic polypectomy, especially for patients with polyps of 6-9 mm size.
| S-EPMC9213389 | biostudies-literature