Publications

BibTex format

@article{Fortner:2017:10.1002/ijc.30560,
author = {Fortner, RT and Husing, A and Kuhn, T and Konar, M and Overvad, K and Tjonneland, A and Hansen, L and Boutron-Ruault, MC and Severi, G and Fournier, A and Boeing, H and Trichopoulou, A and Benetou, V and Orfanos, P and Masala, G and Agnoli, C and Mattiello, A and Tumino, R and Sacerdote, C and Bueno-de-Mesquita, B and Peeters, PHM and Weiderpass, E and Gram, IT and Gavrilyuk, O and Quiros, JR and Huerta, JM and Ardanaz, E and Larranaga, N and Lujan-Barroso, L and Sanchez-Cantalejo, E and Tuna, Butt S and Borgquist, S and Idahl, A and Lundin, E and Khaw, KT and Allen, NE and Rinaldi, S and Dossus, L and Gunter, M and Merritt, MA and Tzoulaki, I and Riboli, E and Kaaks, R},
doi = {10.1002/ijc.30560},
journal = {International Journal of Cancer},
pages = {1317--1323},
title = {Endometrial cancer risk prediction including serum-based biomarkers: Results from the EPIC cohort},
url = {http://dx.doi.org/10.1002/ijc.30560},
volume = {140},
year = {2017}
}

Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Endometrial cancer risk prediction models including lifestyle, anthropometric, and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines, and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p<0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha, and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination.
AU  - Fortner,RT
AU  - Husing,A
AU  - Kuhn,T
AU  - Konar,M
AU  - Overvad,K
AU  - Tjonneland,A
AU  - Hansen,L
AU  - Boutron-Ruault,MC
AU  - Severi,G
AU  - Fournier,A
AU  - Boeing,H
AU  - Trichopoulou,A
AU  - Benetou,V
AU  - Orfanos,P
AU  - Masala,G
AU  - Agnoli,C
AU  - Mattiello,A
AU  - Tumino,R
AU  - Sacerdote,C
AU  - Bueno-de-Mesquita,B
AU  - Peeters,PHM
AU  - Weiderpass,E
AU  - Gram,IT
AU  - Gavrilyuk,O
AU  - Quiros,JR
AU  - Huerta,JM
AU  - Ardanaz,E
AU  - Larranaga,N
AU  - Lujan-Barroso,L
AU  - Sanchez-Cantalejo,E
AU  - Tuna,Butt S
AU  - Borgquist,S
AU  - Idahl,A
AU  - Lundin,E
AU  - Khaw,KT
AU  - Allen,NE
AU  - Rinaldi,S
AU  - Dossus,L
AU  - Gunter,M
AU  - Merritt,MA
AU  - Tzoulaki,I
AU  - Riboli,E
AU  - Kaaks,R
DO  - 10.1002/ijc.30560
EP  - 1323
PY  - 2017///
SN  - 1097-0215
SP  - 1317
TI  - Endometrial cancer risk prediction including serum-based biomarkers: Results from the EPIC cohort
T2  - International Journal of Cancer
UR  - http://dx.doi.org/10.1002/ijc.30560
UR  - http://hdl.handle.net/10044/1/43237
VL  - 140
ER  -

Download

Professor Amanda Cross

Contact

Assistant

Location

Summary

Citation

BibTex format

RIS format (EndNote, RefMan)