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• W4220914693 abstract "Abstract Background and Aim The risk‐adapted screening strategy showed satisfying colorectal cancer (CRC) screening yield and efficiency. We therefore further explored the diagnostic performance variation of this strategy using different risk prediction models. Methods A literature search was conducted to identify studies evaluating risk models for advanced colorectal neoplasm (ACN). The included models were retrospectively validated in a subset sample ( N = 3219) from a population‐based CRC screening trial in China. Diagnosis‐related indictors were compared between the risk‐adapted screening strategy using different models and the fecal immunochemical test (FIT)‐only strategy. For simulated populations with ACN prevalence of 3% to 12%, the trade‐off of additional false positives for each additional true positive were calculated. Results We included 14 eligible risk models with the area under the curves ranging 0.570 to 0.652 in the validation set. The overall sensitivities of the risk‐adapted screening strategy using different risk models for ACN varied from 46.0% to 69.8%, higher than FIT (21.9%), but at the expense of specificities (51.6% to 78.3% vs 97.1%). For population having ACN prevalence of 3%, risk‐adapted screening strategies needed 20.5 to 31.1 additional false positives for each additional true positive compared with FIT, and respective number would substantially reduce (4.7 to 7.1) as the ACN prevalence increased to 12%. Conclusions Risk‐adapted screening strategy using the current risk models showed improved sensitivity for ACN compared with FIT, at the cost of increased colonoscopy workload. The optimal strategy for screening practice should be tailored considering the disease burden and availability of healthcare resources." @default.
• W4220914693 created "2022-04-03" @default.
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• W4220914693 date "2022-03-17" @default.
• W4220914693 modified "2023-10-03" @default.
• W4220914693 title "Head‐to‐head comparison of a risk‐adapted screening strategy using various risk prediction models in detecting colorectal neoplasm" @default.
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• W4220914693 doi "https://doi.org/10.1111/jgh.15825" @default.
• W4220914693 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35263809" @default.
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