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Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography.

ABSTRACT: OBJECTIVE:To determine the association between pericolonic fat and colorectal polyps using CT colonography (CTC). METHODS:A total of 1169 patients who underwent CTC and optical colonoscopy on the same day were assessed. Pericolonic fat was measured on CTC in a band surrounding the colon. Visceral adipose tissue volume was measured at the L2-L3 levels. Student's t-tests, odds ratio, logistic regression, binomial statistics, and weighted kappa were performed to ascertain associations with the incidence of colorectal polyps. RESULTS:Pericolonic fat volume fractions (PFVF) were 61.5 ± 11.0% versus 58.1 ± 11.5%, 61.6 ± 11.1% versus 58.7 ± 11.5%, and 62.4 ± 10.6% versus 58.8 ± 11.5% for patients with and without any polyps, adenomatous polyps, and hyperplastic polyps, respectively (P<0.0001). Similar trends were observed when examining visceral fat volume fractions (VFVF). When patients were ordered by quintiles of PFVF or VFVF, there were 2.49-, 2.19-, and 2.39-fold increases in odds ratio for the presence of any polyp, adenomatous polyps, or hyperplastic polyps from the first to the fifth quintile for PFVF and 1.92-, 2.00-, and 1.71-fold increases in odds ratio for VFVF. Polyps tended to occur more commonly in parts of the colon that had more PFVF than the spatially adjusted average for patients in the highest quintile of VFVF. CONCLUSIONS:Pericolonic fat accumulations, like visceral fat, are correlated with an increased risk of adenomatous and hyperplastic polyps.

SUBMITTER: Liu J

PROVIDER: S-EPMC4310815 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography.

Liu Jiamin J Pattanaik Sanket S Yao Jianhua J Dwyer Andrew J AJ Pickhardt Perry J PJ Choi J Richard JR Summers Ronald M RM

Obesity (Silver Spring, Md.) 20141231 2

<h4>Objective</h4>To determine the association between pericolonic fat and colorectal polyps using CT colonography (CTC).<h4>Methods</h4>A total of 1169 patients who underwent CTC and optical colonoscopy on the same day were assessed. Pericolonic fat was measured on CTC in a band surrounding the colon. Visceral adipose tissue volume was measured at the L2-L3 levels. Student's t-tests, odds ratio, logistic regression, binomial statistics, and weighted kappa were performed to ascertain association ...[more]

PMID: 25558027

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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.

Project description:The clinical relevance and in-vivo growth rates of small (6-9 mm) colorectal polyps are not well established. We aimed to assess the behaviour of such polyps with CT colonography assessments.In this longitudinal study, we enrolled asymptomatic adults undergoing routine colorectal cancer screening with CT colonography at two medical centres in the USA. Experienced investigators (PJP, DHK, JLH) measured volumes and maximum linear sizes of polyps in vivo with CT colonography scans at baseline and surveillance follow-up. We defined progression, stability, and regression on the basis of a 20% volumetric change per year from baseline (20% or more growth classed as progression, 20% growth to -20% reduction classed as stable, and -20% or more reduction classed as regression). We compared findings with histological subgroups confirmed after colonoscopy when indicated. This study is registered with ClinicalTrials.gov, number NCT00204867.Between April, 2004, and June, 2012, we screened 22,006 asymptomatic adults and included 243 adults (mean age 57·4 years [SD 7·1] and median age 56 years [IQR 52-61]; 106 [37%] women), with 306 small colorectal polyps. The mean surveillance interval was 2·3 years (SD 1·4; range 1-7 years; median 2·0 years [IQR 1·1-2·3]). 68 (22%) of 306 polyps progressed, 153 (50%) were stable, and 85 (28%) regressed, including an apparent resolution in 32 (10%) polyps. We established immediate histology in 131 lesions on colonoscopy after final CT colonography. 21 (91%) of 23 proven advanced adenomas progressed, compared with 31 (37%) of 84 proven non-advanced adenomas, and 15 (8%) of 198 other lesions (p<0·0001). The odds ratio for a growing polyp at CT colonography surveillance to become an advanced adenoma was 15·6 (95% CI 7·6-31·7) compared with 6-9 mm polyps detected and removed at initial CT colonography screening (without surveillance). Mean polyp volume change was a 77% increase per year for 23 proven advanced adenomas and a 16% increase per year for 84 proven non-advanced adenomas, but a 13% decrease per year for all proven non-neoplastic or unresected polyps (p<0·0001). An absolute polyp volume of more than 180 mm(3) at surveillance CT colonography identified proven advanced neoplasia (including one delayed cancer) with a sensitivity of 92% (22 of 24 polyps), specificity of 94% (266 of 282 polyps), positive-predictive value of 58% (22 of 38 polyps), and negative-predictive value of 99% (266 of 268 polyps). Only 16 (6%) of the 6-9 mm polyps exceeded 10 mm at follow-up.Volumetric growth assessment of small colorectal polyps could be a useful biomarker for determination of clinical importance. Advanced adenomas show more rapid growth than non-advanced adenomas, whereas most other small polyps remain stable or regress. Our findings might allow for less invasive surveillance strategies, reserving polypectomy for lesions that show substantial growth. Further research is needed to provide more information regarding the ultimate fate of unresected small polyps without significant growth.US National Institutes of Health, National Cancer Institute.

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Dataset Information

Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography.

Publications

Associations among pericolonic fat, visceral fat, and colorectal polyps on CT colonography.

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