Evidence map›Paper›PMID 25742479›Full record

ArticleBritish journal of cancer2015

An epidemiologic risk prediction model for ovarian cancer in Europe: the EPIC study.

K Li, A Hüsing, R T Fortner, A Tjønneland, L Hansen, L Dossus, J Chang-Claude, M Bergmann, A Steffen, C Bamia and 26 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in British journal of cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Improving ovarian cancer risk assessment using a machine learning model developed on data from the Prostate Lung Colorectal Ovarian Cancer Screening Trial.International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2026
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  9. Epidemiology and risk factors for ovarian cancer.Przeglad menopauzalny = Menopause review · 2023
    Review
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  12. Proliferation of the Fallopian Tube Fimbriae and Cortical Inclusion Cysts: Effects of the Menstrual Cycle and the Levonorgestrel Intrauterine Contraceptive System.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

36 authors.

K LiDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
A HüsingDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
R T FortnerDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
A TjønnelandThe Danish Cancer Society Research Center, Copenhagen, Denmark.
L HansenThe Danish Cancer Society Research Center, Copenhagen, Denmark.
L Dossus1] Inserm, Centre for research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health team, F-94805 Villejuif, France [2] University Paris Sud, UMRS 1018, F-94805 Villejuif, France.
J Chang-ClaudeDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
M BergmannGerman Institute of Human Nutrition in Potsdam-Rehbruecke, Potsdam, Germany.
A SteffenGerman Institute of Human Nutrition in Potsdam-Rehbruecke, Potsdam, Germany.
C BamiaDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece.
D Trichopoulos1] Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA [2] Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece [3] Hellenic Health Foundation, Athens, Greece.
A Trichopoulou1] Bureau of Epidemiologic Research, Academy of Athens, Athens, Greece [2] Hellenic Health Foundation, Athens, Greece.
D PalliMolecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute-ISPO, Florence, Italy.
A MattielloDipartimento di Medicina Clinica e Chirurgia, University of Naples Federico II, Naples, Italy.
C AgnoliEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
R TuminoCancer Registry and Histopathology Unit, 'Civic-M.P. Arezzo' Hospital, Ragusa, Italy.
N C Onland-MoretDepartment of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
P H Peeters1] Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands [2] MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
H B Bueno-de-MesquitaDepartment for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.
I T GramDepartment of Community Medicine, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway.
E Weiderpass1] Department of Community Medicine, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway [2] Department of Research, Cancer Registry of Norway, Oslo, Norway [3] Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
E Sánchez-Cantalejo1] Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria de Granada (Granada.ibs), Granada, Spain [2] CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
M-D Chirlaque1] CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain [2] Department of Epidemiology, Murcia Regional Health Authority, Murcia, Spain.
E J DuellUnit of Nutrition, Environment and Cancer Epidemiology Research Program, Bellvitge Biomedical Research Institute (IDIBELL), Catalan Institute of Oncology (ICO), Barcelona, Spain.
E Ardanaz1] CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain [2] Navarre Public Health Institute, Pamplona, Spain.
A IdahlDepartment of Clinical Sciences, Obstetrics and Gynecology and Department of Public Health and Clinical Medicine, Nutritional Research Umeå University, Umeå, Sweden.
E LundinDepartment of Medical Biosciences, Pathology Umeå University, Umeå, Sweden.
K-T KhawUniversity of Cambridge, School of Clinical Medicine, Cambridge, UK.
R C TravisCancer Epidemiology Unit, Nuffield Department of Population Health University of Oxford, Oxford, UK.
M A MerrittDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK.
M J GunterDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK.
E RiboliDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK.
P FerrariInternational Agency for Research on Cancer, Lyon, France.
K Terry1] Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA [2] Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
D Cramer1] Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA [2] Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
R KaaksDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Funding

Screening and Risk Biomarkers for Ovarian Cancer in EPIC SpecimensR01CA158119 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI CRAMER, DANIEL WILLIAM · 2012 to 2015
$2.0M
Cancer Research UK 14136Cancer Research UK 16491Medical Research Council G0401527Medical Research Council G1000143NCI NIH HHS 1R01CA158119NCI NIH HHS R01 CA158119World Health Organization 001
6 · The paper itself

Abstract

backgroundOvarian cancer has a high case-fatality ratio, largely due to late diagnosis. Epidemiologic risk prediction models could help identify women at increased risk who may benefit from targeted prevention measures, such as screening or chemopreventive agents.

methodsWe built an ovarian cancer risk prediction model with epidemiologic risk factors from 202,206 women in the European Prospective Investigation into Cancer and Nutrition study.

resultsOlder age at menopause, longer duration of hormone replacement therapy, and higher body mass index were included as increasing ovarian cancer risk, whereas unilateral ovariectomy, longer duration of oral contraceptive use, and higher number of full-term pregnancies were decreasing risk. The discriminatory power (overall concordance index) of this model, as examined with five-fold cross-validation, was 0.64 (95% confidence interval (CI): 0.57, 0.70). The ratio of the expected to observed number of ovarian cancer cases occurring in the first 5 years of follow-up was 0.90 (293 out of 324, 95% CI: 0.81-1.01), in general there was no evidence for miscalibration.

conclusionOur ovarian cancer risk model containing only epidemiological data showed modest discriminatory power for a Western European population. Future studies should consider adding informative biomarkers to possibly improve the predictive ability of the model.

Indexed as

AgedAged, 80 and overCohort StudiesEuropeFemaleHumansMiddle AgedOvarian NeoplasmsRisk FactorsSurveys and Questionnaires

Identifiers

PMID25742479
PMCPMC4385951

What OpenQuestion holds

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LicenceCC BY-NC-SA
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.