Impact of systematic false-negative test results on the performance of faecal occult blood screening

Risk Scoring Systems for Predicting the Presence of Colorectal Neoplasia by Fecal Immunochemical Test Results in Chinese Population

INTRODUCTION

Adherence to colonoscopy screening for colorectal cancer (CRC) is low in general populations, including those tested positive in the fecal immunochemical test (FIT). Developing tailored risk scoring systems by FIT results may allow for more accurate identification of individuals for colonoscopy.

METHODS

Among 807,109 participants who completed the primary tests in the first-round Shanghai CRC screening program, 71,023 attended recommended colonoscopy. Predictors for colorectal neoplasia were used to develop respective scoring systems for FIT-positive or FIT-negative populations using logistic regression and artificial neural network methods.

RESULTS

Age, sex, area of residence, history of mucus or bloody stool, and CRC in first-degree relatives were identified as predictors for CRC in FIT-positive subjects, while a history of chronic diarrhea and prior cancer were additionally included for FIT-negative subjects. With an area under the receiver operating characteristic curve of more than 0.800 in predicting CRC, the logistic regression-based systems outperformed the artificial neural network-based ones and had a sensitivity of 68.9%, a specificity of 82.6%, and a detection rate of 0.24% by identifying 17.6% subjects at high risk. We also reported an area under the receiver operating characteristic curve of about 0.660 for the systems predicting CRC and adenoma, with a sensitivity of 57.8%, a specificity of 64.6%, and a detection rate of 6.87% through classifying 38.1% subjects as high-risk individuals. The performance of the scoring systems for CRC was superior to the currently used method in Mainland, China, and comparable with the scoring systems incorporating the FIT results.

DISCUSSION

The tailored risk scoring systems may better identify high-risk individuals of colorectal neoplasia and facilitate colonoscopy follow-up. External validation is warranted for widespread use of the scoring systems.

Nonbleeding adenomas: Evidence of systematic false-negative fecal immunochemical test results and their implications for screening effectiveness-A modeling study

BACKGROUND

If some adenomas do not bleed over several years, they will cause systematic false-negative fecal immunochemical test (FIT) results. The long-term effectiveness of FIT screening has been estimated without accounting for such systematic false-negativity. There are now data with which to evaluate this issue.

METHODS

The authors developed one microsimulation model (MISCAN [MIcrosimulation SCreening ANalysis]-Colon) without systematic false-negative FIT results and one model that allowed a percentage of adenomas to be systematically missed in successive FIT screening rounds. Both variants were adjusted to reproduce the first-round findings of the Dutch CORERO FIT screening trial. The authors then compared simulated detection rates in the second screening round with those observed, and adjusted the simulated percentage of systematically missed adenomas to those data. Finally, the authors calculated the impact of systematic false-negative FIT results on the effectiveness of repeated FIT screening.

RESULTS

The model without systematic false-negativity simulated higher detection rates in the second screening round than observed. These observed rates could be reproduced when assuming that FIT systematically missed 26% of advanced and 73% of nonadvanced adenomas. To reduce the false-positive rate in the second round to the observed level, the authors also had to assume that 30% of false-positive findings were systematically false-positive. Systematic false-negative FIT testing limits the long-term reduction of biennial FIT screening in the incidence of colorectal cancer (35.6% vs 40.9%) and its mortality (55.2% vs 59.0%) in participants.

CONCLUSIONS

The results of the current study provide convincing evidence based on the combination of real-life and modeling data that a percentage of adenomas are systematically missed by repeat FIT screening. This impairs the efficacy of FIT screening. Cancer 2016;122:1680-8. © 2016 American Cancer Society.

Calibration methods used in cancer simulation models and suggested reporting guidelines

Increasingly, computer simulation models are used for economic and policy evaluation in cancer prevention and control. A model's predictions of key outcomes, such as screening effectiveness, depend on the values of unobservable natural history parameters. Calibration is the process of determining the values of unobservable parameters by constraining model output to replicate observed data. Because there are many approaches for model calibration and little consensus on best practices, we surveyed the literature to catalogue the use and reporting of these methods in cancer simulation models. We conducted a MEDLINE search (1980 through 2006) for articles on cancer-screening models and supplemented search results with articles from our personal reference databases. For each article, two authors independently abstracted pre-determined items using a standard form. Data items included cancer site, model type, methods used for determination of unobservable parameter values and description of any calibration protocol. All authors reached consensus on items of disagreement. Reviews and non-cancer models were excluded. Articles describing analytical models, which estimate parameters with statistical approaches (e.g. maximum likelihood) were catalogued separately. Models that included unobservable parameters were analysed and classified by whether calibration methods were reported and if so, the methods used. The review process yielded 154 articles that met our inclusion criteria and, of these, we concluded that 131 may have used calibration methods to determine model parameters. Although the term 'calibration' was not always used, descriptions of calibration or 'model fitting' were found in 50% (n = 66) of the articles, with an additional 16% (n = 21) providing a reference to methods. Calibration target data were identified in nearly all of these articles. Other methodological details, such as the goodness-of-fit metric, were discussed in 54% (n = 47 of 87) of the articles reporting calibration methods, while few details were provided on the algorithms used to search the parameter space. Our review shows that the use of cancer simulation modelling is increasing, although thorough descriptions of calibration procedures are rare in the published literature for these models. Calibration is a key component of model development and is central to the validity and credibility of subsequent analyses and inferences drawn from model predictions. To aid peer-review and facilitate discussion of modelling methods, we propose a standardized Calibration Reporting Checklist for model documentation.