Abstract
BACKGROUND: Patients with platinum-sensitive recurrent ovarian cancer are a heterogeneous group, and it is not possible to accurately predict the progression-free survival (PFS) in these patients. We developed and validated a nomogram to help improve prediction of PFS in patients treated with platinum-based chemotherapy.
METHODS: The nomogram was developed in a training cohort (n=955) from the CALYPSO trial and validated in the AGO-OVAR 2.5 Study (n=340). The proportional-hazards model (nomogram) was based on pre-treatment characteristics.
RESULTS: The nomogram had a concordance index (C-index) of 0.645. Significant predictors were tumour size platinum-chemotherapy-free interval, CA-125, number of organ metastatic sites and white blood count. When the nomogram was applied without CA-125 (CA-125 was not available in validation cohort), the C-indices were 0.624 (training) and 0.594 (validation). When classification was based only on the platinum-chemotherapy-free interval, the indices were 0.571 (training) and 0.560 (validation). The calibration plot in the validation cohort based on four predictors (without CA-125) suggested good agreement between actual and nomogram-predicted 12-month PFS probabilities.
CONCLUSION: This nomogram, using five pre-treatment characteristics, improves prediction of PFS in patients with platinum-sensitive ovarian cancer having platinum-based chemotherapy. It will be useful for the design and stratification of patients in clinical trials and also for counselling patients.
Original language | English |
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Pages (from-to) | 1144-50 |
Journal | British Journal of Cancer |
Volume | 105 |
Issue number | 8 |
DOIs | |
Publication status | Published - 2011 Oct 11 |
Externally published | Yes |
Subject classification (UKÄ)
- Cancer and Oncology
Free keywords
- Adult
- Aged
- Aged, 80 and over
- Antineoplastic Agents/therapeutic use
- Carboplatin/therapeutic use
- Cohort Studies
- Disease-Free Survival
- Female
- Humans
- Middle Aged
- Ovarian Neoplasms/drug therapy
- Prognosis
- Proportional Hazards Models