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A prognostic model for advanced colorectal neoplasia recurrence

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Abstract

Purpose

Following colonoscopic polypectomy, US Multisociety Task Force (USMSTF) guidelines stratify patients based on risk of subsequent advanced neoplasia (AN) using number, size, and histology of resected polyps, but have only moderate sensitivity and specificity. We hypothesized that a state-of-the-art statistical prediction model might improve identification of patients at high risk of future AN and address these challenges.

Methods

Data were pooled from seven prospective studies which had follow-up ascertainment of metachronous AN within 3–5 years of baseline polypectomy (combined n = 8,228). Pooled data were randomly split into training (n = 5,483) and validation (n = 2,745) sets. A prognostic model was developed using best practices. Two risk cut-points were identified in the training data which achieved a 10 percentage point improvement in sensitivity and specificity, respectively, over current USMSTF guidelines. Clinical benefit of USMSTF versus model-based risk stratification was then estimated using validation data.

Results

The final model included polyp location, prior polyp history, patient age, and number, size and histology of resected polyps. The first risk cut-point improved sensitivity but with loss of specificity. The second risk cut-point improved specificity without loss of sensitivity (specificity 46.2 % model vs. 42.1 % guidelines, p < 0.001; sensitivity 75.8 % model vs. 74.0 % guidelines, p = 0.64). Estimated AUC was 65 % (95 % CI: 62–69 %).

Conclusion

This model-based approach allows flexibility in trading sensitivity and specificity, which can optimize colonoscopy over- versus underuse rates. Only modest improvements in prognostic power are possible using currently available clinical data. Research considering additional factors such as adenoma detection rate for risk prediction appears warranted.

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Abbreviations

AN:

Advanced neoplasia

AUC:

Area under the receiver operating characteristic curve

BMI:

Body mass index

CI:

Confidence intervals

CRC:

Colorectal cancer

LASSO:

L1-regularized logistic regression model

NRI:

Net reclassification improvement

ROC:

Receiver operating characteristic

USMSTF:

US Multisociety Task Force

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Acknowledgments

This work was supported in part by Public Health Service Grants, CA-41108, CA-23074, CA95060, CA37287, CA104869, CA23108, CA59005, CA26852, and 5R01CA155293 from the National Cancer Institute. Funding for the Veteran’s Affairs Study was supported by the Cooperative Studies Program, Department of Veterans Affairs. The project described was also supported by a pilot grant from the UCSD Department of Family Medicine and Public Health (Liu, PI), as well as in part by Merit Review Award number 1 I01 HX001574-01A1 (Gupta, PI) from the United States Department of Veterans Affairs Health Services Research and Development Service of the VA Office of Research and Development. The views expressed in this article are those of the author(s) and do not necessarily represent the views of the Department of Veterans Affairs.

Author contributions

L.L., K.M., M.E.M., and S.G. contributed to study concept and design. L.L., K.M., and S.G. drafted the manuscript. L.L. and K.M performed the statistical analysis. L.L., K.M., J.A.B., D.A.L., A.J.C., M.G., M.E.M., and S.G. obtained the funding All authors contributed to acquisition, analysis, and interpretation of data, critically revised the manuscript for important intellectual content, and approved the final version of the article, including the authorship list.

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Authors and Affiliations

  1. Division of Biostatistics and Bioinformatics, Department of Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA

    Lin Liu, Karen Messer & Maria Elena Martinez

  2. University of California San Diego Moores Cancer Center, La Jolla, CA, USA

    Lin Liu, Karen Messer, Maria Elena Martinez & Samir Gupta

  3. Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA

    John A. Baron

  4. Division of Gastroenterology and Hepatology, Veterans Affairs Medical Center and Oregon Health and Science University, Portland, OR, USA

    David A. Lieberman

  5. University of Arizona Cancer Center, Arizona College of Public Health, University of Arizona, Tucson, AZ, USA

    Elizabeth T. Jacobs

  6. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    Amanda J. Cross

  7. Cancer Screening and Prevention Research Group, Department of Surgery and Cancer, Imperial College London, London, UK

    Amanda J. Cross

  8. Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Rockville, MD, USA

    Gwen Murphy

  9. Veteran Affairs San Diego Healthcare System, 3350 La Jolla Village Dr, MC 111D, San Diego, CA, 92161, USA

    Samir Gupta

  10. Division of Gastroenterology, Department of Internal Medicine, University of California San Diego, La Jolla, CA, USA

    Samir Gupta

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  1. Lin Liu
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  2. Karen Messer
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  8. Maria Elena Martinez
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  9. Samir Gupta
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Correspondence to Samir Gupta.

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Liu, L., Messer, K., Baron, J.A. et al. A prognostic model for advanced colorectal neoplasia recurrence. Cancer Causes Control 27, 1175–1185 (2016). https://doi.org/10.1007/s10552-016-0795-5

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  • DOI: https://doi.org/10.1007/s10552-016-0795-5

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