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

Li K, Hüsing A, Fortner RT, Tjønneland A, Hansen L, Dossus L, Chang-Claude J, Bergmann M, Steffen A, Bamia C, Trichopoulos D, Trichopoulou A, Palli D, Mattiello A, Agnoli C, Tumino R, Onland-Moret NC, Peeters PH, Bueno-de-Mesquita HB, Gram IT, Weiderpass E, Sánchez-Cantalejo E, Chirlaque MD, Duell EJ, Ardanaz E, Idahl A, Lundin E, Khaw KT, Travis RC, Merritt MA, Gunter MJ, Riboli E, Ferrari P, Terry K, Cramer D, Kaaks R - Br. J. Cancer (2015)

Bottom Line: Older 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).Future studies should consider adding informative biomarkers to possibly improve the predictive ability of the model.

View Article: PubMed Central - PubMed

Affiliation: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

ABSTRACT

Background: Ovarian 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.

Methods: We 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.

Results: Older 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.

Conclusion: Our 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.

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Related in: MedlinePlus

Predicted versus observed number of cases in the first 5 years of the follow-up by risk deciles.
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fig2: Predicted versus observed number of cases in the first 5 years of the follow-up by risk deciles.

Mentions: The C-index from cross-validation was 0.64 (95% CI: 0.58, 0.70) for the full model and 0.64 (95% CI: 0.57, 0.70) for the selected model (Table 3), implying a modest discrimination. With respect to calibration, the selected model predicted 293 ovarian cancers to occur during the first 5 years of follow-up, in contrast to 324 cases that were actually observed (E/O=0.90; 95% CI: 0.81, 1.01). This underestimation can be observed in eight decile groups in the calibration plot of the selected model (Figure 2), although the H-L test gave no evidence for miscalibration in general (P=0.14). With estimates ∼0.9, the calibration slope confirmed the above described tendency of overfitting, but the CIs do not indicate significance.


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

Li K, Hüsing A, Fortner RT, Tjønneland A, Hansen L, Dossus L, Chang-Claude J, Bergmann M, Steffen A, Bamia C, Trichopoulos D, Trichopoulou A, Palli D, Mattiello A, Agnoli C, Tumino R, Onland-Moret NC, Peeters PH, Bueno-de-Mesquita HB, Gram IT, Weiderpass E, Sánchez-Cantalejo E, Chirlaque MD, Duell EJ, Ardanaz E, Idahl A, Lundin E, Khaw KT, Travis RC, Merritt MA, Gunter MJ, Riboli E, Ferrari P, Terry K, Cramer D, Kaaks R - Br. J. Cancer (2015)

Predicted versus observed number of cases in the first 5 years of the follow-up by risk deciles.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385951&req=5

fig2: Predicted versus observed number of cases in the first 5 years of the follow-up by risk deciles.
Mentions: The C-index from cross-validation was 0.64 (95% CI: 0.58, 0.70) for the full model and 0.64 (95% CI: 0.57, 0.70) for the selected model (Table 3), implying a modest discrimination. With respect to calibration, the selected model predicted 293 ovarian cancers to occur during the first 5 years of follow-up, in contrast to 324 cases that were actually observed (E/O=0.90; 95% CI: 0.81, 1.01). This underestimation can be observed in eight decile groups in the calibration plot of the selected model (Figure 2), although the H-L test gave no evidence for miscalibration in general (P=0.14). With estimates ∼0.9, the calibration slope confirmed the above described tendency of overfitting, but the CIs do not indicate significance.

Bottom Line: Older 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).Future studies should consider adding informative biomarkers to possibly improve the predictive ability of the model.

View Article: PubMed Central - PubMed

Affiliation: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.

ABSTRACT

Background: Ovarian 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.

Methods: We 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.

Results: Older 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.

Conclusion: Our 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.

Show MeSH
Related in: MedlinePlus