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@ARTICLE{Peng:144699,
author = {L. Peng$^*$ and Y. Balavarca$^*$ and K. Weigl$^*$ and M.
Hoffmeister$^*$ and H. Brenner$^*$},
title = {{H}ead-to-{H}ead {C}omparison of the {P}erformance of 17
{R}isk {M}odels for {P}redicting {P}resence of {A}dvanced
{N}eoplasms in {C}olorectal {C}ancer {S}creening.},
journal = {The American journal of gastroenterology},
volume = {114},
number = {9},
issn = {0002-9270},
address = {London [u.a.]},
publisher = {Nature},
reportid = {DKFZ-2019-02141},
pages = {1520-1530},
year = {2019},
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.},
cin = {C070 / C120 / L101},
ddc = {610},
cid = {I:(DE-He78)C070-20160331 / I:(DE-He78)C120-20160331 /
I:(DE-He78)L101-20160331},
pnm = {313 - Cancer risk factors and prevention (POF3-313)},
pid = {G:(DE-HGF)POF3-313},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:31464746},
doi = {10.14309/ajg.0000000000000370},
url = {https://inrepo02.dkfz.de/record/144699},
}