Predicting survival in epithelial ovarian cancer.

Boldingh, J.H.L. (2013) Predicting survival in epithelial ovarian cancer. thesis, Medicine.

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Abstract

Objective: Our goal was to accurately predict 5-year survival in patients with epithelial ovarian cancer and to develop a nomogram that incorporated type of primary treatment, each FIGO stage and each histological subtype. Methods: From a prospectively kept database we identified all patients treated for epithelial ovarian cancer in the Center of Gynaecologic Oncology Amsterdam (CGOA). We included patients with all stages of disease who underwent primary or interval debulking surgery for a primary epithelial ovarian tumour between January 1998 and July 2010. Patients with Brenner/Transitional cell tumours and borderline malignant potential subtypes were excluded. The primary outcome of our study was disease specific survival and we considered 12 potential predictors for the Cox proportional hazard model. The performance was evaluated by discrimination and by calibration. Overfitting was corrected by a lasso method and compared to a more standard bootstrap method. Finally, a nomogram was created. Results: In total, 840 patients were eligible for the study and 489 (58%) patients died from EOC. We found that FIGO stage, residual tumour, type of surgery, histology, performance scale, age, amount of ascites and BRCA were predictive of 5-year survival. The final model showed a reasonable discrimination with a c index of 0.71 and an accurate calibration. The overfit was 10 per cent and this corresponded to an overall shrinkage factor of 0.90. Conclusion: This prediction model provides an individual prognosis for patients with epithelial ovarian cancer. When our model performs well at external validation it will be a useful tool for selecting eligible patients for RCTs and it could help in shared decision-making.

Item Type:	Thesis (Thesis)
Supervisor name:	Arts, dr. H.J.G.
Supervisor name:	Rutten, drs. M.J. and Buist, dr. M.R. and AMC
Faculty:	Medical Sciences
Date Deposited:	25 Jun 2020 10:46
Last Modified:	25 Jun 2020 10:46
URI:	https://umcg.studenttheses.ub.rug.nl/id/eprint/723

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