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

Abstract

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.