A prognostic nomogram to predict overall survival in patients with platinum-sensitive recurrent ovarian cancer.

BACKGROUND Patients with platinum-sensitive recurrent ovarian cancer have variable prognosis and survival. We extend previous work on prediction of progression-free survival by developing a nomogram to predict overall survival (OS) in these patients treated with platinum-based chemotherapy. PATIENTS AND METHODS The nomogram was developed using data from the CAELYX in Platinum-Sensitive Ovarian Patients (CALYPSO) trial. Multivariate proportional hazards models were generated based on pre-treatment characteristics to develop a nomogram that classifies patient prognosis based on OS outcome. We also developed two simpler models with fewer variables and conducted model validations in independent datasets from AGO-OVAR Study 2.5 and ICON 4. We compare the performance of the nomogram with the simpler models by examining the differences in the C-statistics and net reclassification index (NRI). RESULTS The nomogram included six significant predictors: interval from last platinum chemotherapy, performance status, size of the largest tumour, CA-125, haemoglobin and the number of organ sites of metastasis (C-statistic 0.67; 95% confidence interval 0.65-0.69). Among the CALPYSO patients, the median OS for good, intermediate and poor prognosis groups was 56.2, 31.0 and 20.8 months, respectively. When CA-125 was not included in the model, the C-statistics were 0.65 (CALYPSO) and 0.64 (AGO-OVAR 2.5). A simpler model (interval from last platinum chemotherapy, performance status and CA-125) produced a significant decrease of the C-statistic (0.63) and NRI (26.4%, P < 0.0001). CONCLUSIONS This nomogram with six pre-treatment characteristics improves OS prediction in patients with platinum-sensitive ovarian cancer and is superior to models with fewer prognostic factors or platinum chemotherapy free interval alone. With independent validation, this nomogram could potentially be useful for improved stratification of patients in clinical trials and also for counselling patients.

[1]  B Richter,et al.  Prognostic nomogram to predict progression-free survival in patients with platinum-sensitive recurrent ovarian cancer , 2011, British Journal of Cancer.

[2]  A. Vickers.  Prediction models: revolutionary in principle, but do they do more good than harm? , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  V. Gebski,et al.  Pegylated liposomal doxorubicin and carboplatin (C-PLD) versus paclitaxel and carboplatin (C-P) in platinum-sensitive ovarian cancer (OC) patients (pts): Treatment at recurrence and overall survival (OS) final analysis from CALYPSO phase III GCIG trial. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[4]  D. Sargent,et al.  Comparison of error rates in single-arm versus randomized phase II cancer clinical trials. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  M. Stockler,et al.  Historical cross-trial comparisons for competing treatments in advanced breast cancer--an empirical analysis of bias. , 2010, European journal of cancer.

[6]  S. Cannistra,et al.  Gene-expression profiling in epithelial ovarian cancer , 2008, Nature Clinical Practice Oncology.

[7]  Donna Niedzwiecki,et al.  Meta-analysis of phase II cooperative group trials in metastatic stage IV melanoma to determine progression-free and overall survival benchmarks for future phase II trials. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[9]  J. Thigpen.  Gemcitabine Plus Carboplatin Compared With Carboplatin in Patients With Platinum-Sensitive Recurrent Ovarian Cancer: An Intergroup Trial of the AGO-OVAR, the NCIC CTG, and the EORTC GCG , 2008 .

[10]  M. Carey,et al.  The prognostic effects of performance status and quality of life scores on progression-free survival and overall survival in advanced ovarian cancer. , 2008, Gynecologic oncology.

[11]  Katherine S Panageas,et al.  When you look matters: the effect of assessment schedule on progression-free survival. , 2007, Journal of the National Cancer Institute.

[12]  M. Stockler,et al.  Disarming the guarded prognosis: predicting survival in newly referred patients with incurable cancer , 2005, British Journal of Cancer.

[13]  N Keiding,et al.  Individual survival time prediction using statistical models , 2005, Journal of Medical Ethics.

[14]  E. Eisenhauer,et al.  Re: New guidelines to evaluate the response to treatment in solid tumors (ovarian cancer). , 2005, Journal of the National Cancer Institute.

[15]  Steven Sun,et al.  Long-term survival advantage for women treated with pegylated liposomal doxorubicin compared with topotecan in a phase 3 randomized study of recurrent and refractory epithelial ovarian cancer. , 2004, Gynecologic oncology.

[16]  M. Pencina,et al.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation , 2004, Statistics in medicine.

[17]  Phyllis N Butow,et al.  Cancer patient preferences for communication of prognosis in the metastatic setting. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[18]  M. Parmar,et al.  Erratum: The ICON and AGO Collaborators. Paclitaxel plus platinum-based chemotherapy versus conventional platinum-based chemotherapy in women with relapsed ovarian cancer: The ICON4/AGO-OVAR-2.2 trial (Lancet (2003) 361 (2099-106)) , 2003 .

[19]  Paul Glare,et al.  A systematic review of physicians' survival predictions in terminally ill cancer patients , 2003, BMJ : British Medical Journal.

[20]  V. Torri,et al.  Paclitaxel plus platinum-based chemotherapy versus conventional platinum-based chemotherapy in women with relapsed ovarian cancer: the ICON4/AGO-OVAR-2.2 trial , 2003, The Lancet.

[21]  Roger Newson,et al.  Parameters behind “Nonparametric” Statistics: Kendall's tau, Somers’ D and Median Differences , 2002 .

[22]  P. Ravdin,et al.  Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  B J McNeil,et al.  Staging of advanced ovarian cancer: comparison of imaging modalities--report from the Radiological Diagnostic Oncology Group. , 2000, Radiology.

[24]  A. Vigano,et al.  The relative accuracy of the clinical estimation of the duration of life for patients with end of life cancer , 1999, Cancer.

[25]  E. Cook,et al.  Relationship between cancer patients' predictions of prognosis and their treatment preferences. , 1998, JAMA.

[26]  Q. Mcnemar.  Note on the sampling error of the difference between correlated proportions or percentages , 1947, Psychometrika.

[27]  D.,et al.  Regression Models and Life-Tables , 2022 .