Prediction Model for Brain Metastasis in Patients With Metastatic Germ-Cell Tumors

Background

Brain metastasis (BM) is an independent adverse prognostic factor in metastatic germ cell tumors (mGCT). We aimed to establish an effective and practical BM prediction model.

Patients and Methods

Between January 1990 and September 2017, 2291 patients with mGCT who were treated at Indiana University were identified. Patients were divided into two categories: BM present (N = 154) and BM absent (N = 2137). Kaplan–Meier methods were used to analyze progression free survival (PFS) and overall survival (OS). Logistic regression was used to determine a predictive model for whether BM was present. The data was separated into training and validation datasets with equal numbers of events in each.

Results

The 2-year PFS and OS for patients with versus without BM: 17% versus 65% (p < 0.001) and 62% versus 91% (p < 0.001) respectively. Among the 154 patients with BM, 64 (42%) had radiation only (whole-brain radiotherapy or gamma knife), 22 (14%) had BM-surgery only, 14 (9%) had both radiation and BM-surgery. 54 patients (35%) did not receive local therapy for BM. Stepwise selection was used to determine the best model with p < 0.15 as the entry and staying criteria. The model with the largest ROC AUC was used moving forward. The model was tested in the validation dataset. A model was generated including age at diagnosis ≥ 40, choriocarcinoma predominant histology, pre-chemotherapy hCG≥ 5000, presence of pulmonary metastases size < 3, or ≥ 3 cm, and presence of bone metastasis. Patients with score of 0, 1, 2, 3, 4, 5, 6, 7, 8 points had a 0.6%, 1.4%, 3.5%, 8.2%, 18.3%, 36%, 58%, 78%, 90% probability of having BM, respectively.

Conclusions

The prediction model developed in this study demonstrated discrimination capability of predicting BM occurrence in mGCT and can be used to identify high-risk patients.