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Conventional Computed Tomographic Calcium Scoring vs full chest CTCS for lung cancer screening: a cost-effectiveness analysis.

ABSTRACT: BackgroundConventional CTCS images the mid/lower chest for coronary artery disease (CAD). Because many CAD patients are also at risk for lung malignancy, CTCS often discovers incidental pulmonary nodules (IPN). CTCS excludes the upper chest, where malignancy is common. Full-chest CTCS (FCT) may be a cost-effective screening tool for IPN.MethodsA decision tree was created to compare a FCT to CTCS in a hypothetical patient cohort with suspected CAD. (Figure) The design compares the effects of missed cancers on CTCS with the cost of working up non-malignant nodules on FCT. The model was informed by results of the National Lung Screening Trial and literature review, including the rate of malignancy among patients receiving CTCS and the rate of malignancy in upper vs lower portions of the lung. The analysis outcomes are Quality-Adjusted Life Year (QALY) and incremental cost-effectiveness ratio (ICER), which is generally considered beneficial when <$50,000/QALY.ResultsLiterature review suggests that rate of IPNs in the upper portion of the lung varied from 47 to 76%. Our model assumed that IPNs occur in upper and lower portions of the lung with equal frequency. The model also assumes an equal malignancy potential in upper lung IPNs despite data that malignancy occurs 61–66% in upper lung fields.In the base case analysis, a FCT will lead to an increase of 0.03 QALYs comparing to conventional CTCS (14.54 vs 14.51 QALY, respectively), which translates into an QALY increase of 16?days. The associated incremental cost for FCT is $278 ($1027 vs $748, FCT vs CTCS respectively. The incremental cost-effectiveness ratio (ICER) is $10,289/QALY, suggesting significant benefit. Sensitivity analysis shows this benefit increases proportional to the rate of malignancy in upper lung fields.ConclusionConventional CTCS may be a missed opportunity to screen for upper lung field cancers in high risk patients. The ICER of FCT is better than screening for breast cancer screening (mammograms $80?k/QALY) and colon cancer (colonoscopy $6?k/QALY). Prospective studies are appropriate to define protocols for FCT.

SUBMITTER: Jiang B

PROVIDER: S-EPMC7336401 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe Centers for Medicare and Medicaid Services (CMS) considered whether to reimburse computed tomographic colonography (CTC) for colorectal cancer screening of Medicare enrollees. To help inform its decision, we evaluated the reimbursement rate at which CTC screening could be cost-effective compared with the colorectal cancer screening tests that are currently reimbursed by CMS and are included in most colorectal cancer screening guidelines, namely annual fecal occult blood test (FOBT), flexible sigmoidoscopy every 5 years, flexible sigmoidoscopy every 5 years in conjunction with annual FOBT, and colonoscopy every 10 years.MethodsWe used three independently developed microsimulation models to assess the health outcomes and costs associated with CTC screening and with currently reimbursed colorectal cancer screening tests among the average-risk Medicare population. We assumed that CTC was performed every 5 years (using test characteristics from either a Department of Defense CTC study or the National CTC Trial) and that individuals with findings of 6 mm or larger were referred to colonoscopy. We computed incremental cost-effectiveness ratios for the currently reimbursed screening tests and calculated the maximum cost per scan (ie, the threshold cost) for the CTC strategy to lie on the efficient frontier. Sensitivity analyses were performed on key parameters and assumptions.ResultsAssuming perfect adherence with all tests, the undiscounted number life-years gained from CTC screening ranged from 143 to 178 per 1000 65-year-olds, which was slightly less than the number of life-years gained from 10-yearly colonoscopy (152-185 per 1000 65-year-olds) and comparable to that from 5-yearly sigmoidoscopy with annual FOBT (149-177 per 1000 65-year-olds). If CTC screening was reimbursed at $488 per scan (slightly less than the reimbursement for a colonoscopy without polypectomy), it would be the most costly strategy. CTC screening could be cost-effective at $108-$205 per scan, depending on the microsimulation model used. Sensitivity analyses showed that if relative adherence to CTC screening was 25% higher than adherence to other tests, it could be cost-effective if reimbursed at $488 per scan.ConclusionsCTC could be a cost-effective option for colorectal cancer screening among Medicare enrollees if the reimbursement rate per scan is substantially less than that for colonoscopy or if a large proportion of otherwise unscreened persons were to undergo screening by CTC.

Project description:ObjectivesCoronary artery calcium (CAC) scores derived from computed tomography (CT) scans are used for cardiovascular risk stratification. Artificial intelligence (AI) can assist in CAC quantification and potentially reduce the time required for human analysis. This study aimed to develop and evaluate a fully automated model that identifies and quantifies CAC.MethodsFully convolutional neural networks for automated CAC scoring were developed and trained on 2439 cardiac CT scans and validated using 771 scans. The model was tested on an independent set of 1849 cardiac CT scans. Agatston CAC scores were further categorised into five risk categories (0, 1-10, 11-100, 101-400, and > 400). Automated scores were compared to the manual reference standard (level 3 expert readers).ResultsOf 1849 scans used for model testing (mean age 55.7 ± 10.5 years, 49% males), the automated model detected the presence of CAC in 867 (47%) scans compared with 815 (44%) by human readers (p = 0.09). CAC scores from the model correlated very strongly with the manual score (Spearman's r = 0.90, 95% confidence interval [CI] 0.89-0.91, p < 0.001 and intraclass correlation coefficient = 0.98, 95% CI 0.98-0.99, p < 0.001). The model classified 1646 (89%) into the same risk category as human observers. The Bland-Altman analysis demonstrated little difference (1.69, 95% limits of agreement: -41.22, 44.60) and there was almost excellent agreement (Cohen's κ = 0.90, 95% CI 0.88-0.91, p < 0.001). Model analysis time was 13.1 ± 3.2 s/scan.ConclusionsThis artificial intelligence-based fully automated CAC scoring model shows high accuracy and low analysis times. Its potential to optimise clinical workflow efficiency and patient outcomes requires evaluation.Key points• Coronary artery calcium (CAC) scores are traditionally assessed using cardiac computed tomography and require manual input by human operators to identify calcified lesions. • A novel artificial intelligence (AI)-based model for fully automated CAC scoring was developed and tested on an independent dataset of computed tomography scans, showing very high levels of correlation and agreement with manual measurements as a reference standard. • AI has the potential to assist in the identification and quantification of CAC, thereby reducing the time required for human analysis.

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Conventional Computed Tomographic Calcium Scoring vs full chest CTCS for lung cancer screening: a cost-effectiveness analysis.

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