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Endometrial cancer risk prediction including serum‐based biomarkers: results from the EPIC cohort
Author(s) -
Fortner Renée T.,
Hüsing Anika,
Kühn Tilman,
Konar Meric,
Overvad Kim,
Tjønneland Anne,
Hansen Louise,
BoutronRuault MarieChristine,
Severi Gianluca,
Fournier Agnès,
Boeing Heiner,
Trichopoulou Antonia,
Benetou Vasiliki,
Orfanos Philippos,
Masala Giovanna,
Agnoli Claudia,
Mattiello Amalia,
Tumino Rosario,
Sacerdote Carlotta,
BuenodeMesquita H.Bas,
Peeters Petra H.M.,
Weiderpass Elisabete,
Gram Inger T.,
Gavrilyuk Oxana,
Quirós J. Ramón,
Maria Huerta José,
Ardanaz Eva,
Larrañaga Nerea,
LujanBarroso Leila,
SánchezCantalejo Emilio,
Butt Salma Tunå,
Borgquist Signe,
Idahl Annika,
Lundin Eva,
Khaw KayTee,
Allen Naomi E.,
Rinaldi Sabina,
Dossus Laure,
Gunter Marc,
Merritt Melissa A.,
Tzoulaki Ioanna,
Riboli Elio,
Kaaks Rudolf
Publication year - 2017
Publication title -
international journal of cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.475
H-Index - 234
eISSN - 1097-0215
pISSN - 0020-7136
DOI - 10.1002/ijc.30560
Subject(s) - epidemiology , cancer , german , medicine , danish , cohort , cohort study , biostatistics , library science , gerontology , oncology , history , philosophy , linguistics , archaeology , computer science
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.