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| Home > Publications database > Head-to-Head Comparison of the Performance of 17 Risk Models for Predicting Presence of Advanced Neoplasms in Colorectal Cancer Screening. |
| Home > Publications database > Head-to-Head Comparison of the Performance of 17 Risk Models for Predicting Presence of Advanced Neoplasms in Colorectal Cancer Screening. |
| Journal Article | DKFZ-2019-02141 |
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2019
Nature
London [u.a.]
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Please use a persistent id in citations: doi:10.14309/ajg.0000000000000370
Abstract: Many risk scores have been proposed to predict presence of advanced colorectal neoplasms, but a comprehensive comparison conducted in the same population is sparse. The aim of this study was to evaluate and directly compare the diagnostic performance of published risk prediction models for advanced colorectal neoplasms.Data were drawn from 2 cohorts of subjects undergoing screening colonoscopy in Germany, i.e., KolosSal (n = 16,195) and BliTz (n = 7,444). Absolute risks and relative risks were generated for the presence of at least 1 advanced neoplasm, taking the lowest risk group as the reference group. Performance of risk models was assessed by the area under the receiver operating characteristic curve (AUC) and compared by the net reclassification improvement.The 2 cohorts included 1,917 (11.8%) and 848 (11.4%) participants with advanced neoplasm, respectively. Absolute risks were mostly between 5% and 10% among participants in the lowest risk group and between 15% and 20% among participants in the highest risk group, and relative risks mostly ranged from 2.0 to 4.0 across the risk models in both cohorts. The AUCs ranged from 0.58 to 0.65 in KolosSal and from 0.57 to 0.61 in BliTz for all risk scores. Compared to models with lower AUC, classification was significantly improved in most models with higher AUC.Risk models for advanced colorectal neoplasms generally yielded modest discriminatory power, despite some variation in performance between models. Future studies should evaluate the performance of these risk models in racially diverse populations and investigate possible extensions, such as combination with polygenic risk scores.
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