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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Predicting outcomes in female germ cell tumors using a modified International Germ Cell Cancer Collaborative Group
Ying L Liu1, Beryl L Manning-Geist2, Andrea Knezevic3
1Department of Medicine, Memorial Sloan Kettering Cancer, New York, NY, USA; Department of Medicine, Weill Cornell Medical College, New York, NY, USA.
Objective:
We previously developed preoperative and pre-chemotherapy modified versions of the male International Germ Cell Cancer Collaborative Group (IGCCCG) prognostic model and assessed it in female patients with germ cell tumors (GCTs). We sought to validate these modified IGCCCG (mIGCCCG) models in a new cohort.
Methods:
We queried institutional databases for female patients with GCTs treated at Memorial Sloan Kettering Cancer Center from 1/1/1990-6/1/2020. The mIGCCCG model classifies patients with non-dysgerminomas as good, intermediate, or poor risk based on tumor markers using male IGCCCG cutoffs and absence/presence of non-pulmonary/peritoneal visceral metastasis. In dysgerminomas, good- and intermediate-risk groups are defined by absence/presence of non-pulmonary/peritoneal visceral metastasis. Progression-free survival (PFS) and overall survival (OS) were estimated for each group in the validation and combined original and validation cohorts. Associations between individual clinical factors and outcomes were evaluated.
Results:
Among 183 female patients with GCTs, clinical characteristics and outcomes were similar between the original (n = 93) and validation (n = 90) cohorts. In multivariable models, higher stage, older age, and non-dysgerminoma histology predicted worse PFS and OS (p < 0.05). Among 162 patients who received chemotherapy, preoperative and pre-chemotherapy mIGCCCG models were significantly associated with PFS and OS (p < 0.001 for all groups). With the preoperative model, 3-year PFS rates were 94%, 76%, and 50% in the good-, intermediate-, and poor-risk patients, respectively; OS rates were 96%, 86%, and 52%, respectively. Even within stage groups, mIGCCCG risk classifications were associated with clinical outcomes.
Conclusions:
A female-specific mIGCCCG risk model effectively stratifies patients and should be incorporated into clinical trials.
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