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Comparative economic evaluation of data from the ACRIN National CT Colonography Trial with three cancer intervention and surveillance modeling network microsimulations.

ABSTRACT: To estimate the cost-effectiveness of computed tomographic (CT) colonography for colorectal cancer (CRC) screening in average-risk asymptomatic subjects in the United States aged 50 years.Enrollees in the American College of Radiology Imaging Network National CT Colonography Trial provided informed consent, and approval was obtained from the institutional review board at each site. CT colonography performance estimates from the trial were incorporated into three Cancer Intervention and Surveillance Modeling Network CRC microsimulations. Simulated survival and lifetime costs for screening 50-year-old subjects in the United States with CT colonography every 5 or 10 years were compared with those for guideline-concordant screening with colonoscopy, flexible sigmoidoscopy plus either sensitive unrehydrated fecal occult blood testing (FOBT) or fecal immunochemical testing (FIT), and no screening. Perfect and reduced screening adherence scenarios were considered. Incremental cost-effectiveness and net health benefits were estimated from the U.S. health care sector perspective, assuming a 3% discount rate.CT colonography at 5- and 10-year screening intervals was more costly and less effective than FOBT plus flexible sigmoidoscopy in all three models in both 100% and 50% adherence scenarios. Colonoscopy also was more costly and less effective than FOBT plus flexible sigmoidoscopy, except in the CRC-SPIN model assuming 100% adherence (incremental cost-effectiveness ratio: $26,300 per life-year gained). CT colonography at 5- and 10-year screening intervals and colonoscopy were net beneficial compared with no screening in all model scenarios. The 5-year screening interval was net beneficial over the 10-year interval except in the MISCAN model when assuming 100% adherence and willingness to pay $50,000 per life-year gained.All three models predict CT colonography to be more costly and less effective than non-CT colonographic screening but net beneficial compared with no screening given model assumptions.

SUBMITTER: Vanness DJ

PROVIDER: S-EPMC3198218 | biostudies-other | 2011 Nov

REPOSITORIES: biostudies-other

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Project description:To determine whether the reader's preference for a primary two-dimensional (2D) or three-dimensional (3D) computed tomographic (CT) colonographic interpretation method affects performance when using each technique.In this institutional review board-approved, HIPAA-compliant study, images from 2531 CT colonographic examinations were interpreted by 15 trained radiologists by using colonoscopy as a reference standard. Through a survey at study start, study end, and 6-month intervals, readers were asked whether their interpretive preference in clinical practice was to perform a primary 2D, primary 3D, or both 2D and 3D interpretation. Readers were randomly assigned a primary interpretation method (2D or 3D) for each CT colonographic examination. Sensitivity and specificity of each method (primary 2D or 3D), for detecting polyps of 10 mm or larger and 6 mm or larger, based on interpretive preference were estimated by using resampling methods.Little change was observed in readers' preferences when comparing them at study start and study end, respectively, as follows: primary 2D (eight and seven readers), primary 3D (one and two readers), and both 2D and 3D (six and six readers). Sensitivity and specificity, respectively, for identifying examinations with polyps of 10 mm or larger for readers with a primary 2D preference (n = 1128 examinations) were 0.84 and 0.86, which was not significantly different from 0.84 and 0.83 for readers who preferred 2D and 3D (n = 1025 examinations) or from 0.76 and 0.82 for readers with a primary 3D preference (n = 378 examinations). When performance by using the assigned 2D or 3D method was evaluated on the basis of 2D or 3D preference, there was no difference among those readers by using their preferred versus not preferred method of interpretation. Similarly, no significant difference among readers or preferences was seen when performance was evaluated for detection of polyps of 6 mm or larger.The reader's preference for interpretive method had no effect on CT colonographic performance.

Project description:To conduct post-hoc analysis of National CT Colonography Trial data and compare the sensitivity and specificity of computed tomographic (CT) colonography in participants younger than 65 years with those in participants aged 65 years and older.Of 2600 asymptomatic participants recruited at 15 centers for the trial, 497 were 65 years of age or older. Approval of this HIPAA-compliant study was obtained from the institutional review board of each site, and informed consent was obtained from each subject. Radiologists certified in CT colonography reported lesions 5 mm in diameter or larger. Screening detection of large (≥10-mm) histologically confirmed colorectal neoplasia was the primary end point; screening detection of smaller (6-9-mm) colorectal neoplasia was a secondary end point. The differences in sensitivity and specificity of CT colonography in the two age cohorts (age < 65 years and age ≥ 65 years) were estimated with bootstrap confidence intervals (CIs).Complete data were available for 477 participants 65 years of age or older (among 2531 evaluable participants). Prevalence of adenomas 1 cm or larger for the older participants versus the younger participants was 6.9% (33 of 477) versus 3.7% (76 of 2054) (P < .004). For large neoplasms, mean estimates for CT colonography sensitivity and specificity among the older cohort were 0.82 (95% CI: 0.644, 0.944) and 0.83 (95% CI: 0.779, 0.883), respectively. For large neoplasms in the younger group, CT colonography sensitivity and specificity were 0.92 (95% CI: 0.837, 0.967) and 0.86 (95% CI: 0.816, 0.899), respectively. Per-polyp sensitivity for large neoplasms for the older and younger populations was 0.75 (95% CI: 0.578, 0.869) and 0.84 (95% CI: 0.717, 0.924), respectively. For the older and younger groups, per-participant sensitivity was 0.72 (95% CI: 0.565, 0.854) and 0.81 (95% CI: 0.745, 0.882) for detecting adenomas 6 mm in diameter or larger.For most measures of diagnostic performance and in most subsets, the difference between senior-aged participants and those younger than 65 years was not statistically significant.

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.

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.

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Comparative economic evaluation of data from the ACRIN National CT Colonography Trial with three cancer intervention and surveillance modeling network microsimulations.

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