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Purpose: Approximately 12,000 glioblastomas are diagnosed annually in the United States. The median survival rate for this disease is 12 months, but individual survival rates can vary with patient-specific factors, including extent of surgical resection (EOR). The goal of our investigation is to develop a reliable strategy for personalized survival prediction and for quantifying the relationship between survival, EOR, and adjuvant chemoradiotherapy., Patients and Methods: We used accelerated failure time (AFT) modeling using data from 721 newly diagnosed patients with glioblastoma (from 1993 to 2010) to model the factors affecting individualized survival after surgical resection, and we used the model to construct probabilistic, patient-specific tools for survival prediction. We validated this model with independent data from 109 patients from a second institution., Results: AFT modeling using age, Karnofsky performance score, EOR, and adjuvant chemoradiotherapy produced a continuous, nonlinear, multivariable survival model for glioblastoma. The median personalized predictive error was 4.37 months, representing a more than 20% improvement over current methods. Subsequent model-based calculations yield patient-specific predictions of the incremental effects of EOR and adjuvant therapy on survival., Conclusion: Nonlinear, multivariable AFT modeling outperforms current methods for estimating individual survival after glioblastoma resection. The model produces personalized survival curves and quantifies the relationship between variables modulating patient-specific survival. This approach provides comprehensive, personalized, probabilistic, and clinically relevant information regarding the anticipated course of disease, the overall prognosis, and the patient-specific influence of EOR and adjuvant chemoradiotherapy. The continuous, nonlinear relationship identified between expected median survival and EOR argues against a surgical management strategy based on rigid EOR thresholds and instead provides the first explicit evidence supporting a maximum safe resection approach to glioblastoma surgery.

Purpose: Population-based estimates of the incidence of brain metastases are not generally available. The purpose of this study was to calculate population-based incidence proportions (IPs) of brain metastases from single primary lung, melanoma, breast, renal, or colorectal cancer., Patients and Methods: Patients diagnosed with single primary lung, melanoma, breast, renal, or colorectal cancer (1973 to 2001) in the Metropolitan Detroit Cancer Surveillance System (MDCSS) were used for analysis. IP of brain metastases by primary site and variable of interest (race, sex, age at diagnosis of primary cancer, and Surveillance, Epidemiology, and End Results [SEER] stage of primary cancer) was calculated with 95% CIs., Results: Total IP percentage (IP%) of brain metastases was 9.6% for all primary sites combined, and highest for lung (19.9%), followed by melanoma (6.9%), renal (6.5%), breast (5.1%), and colorectal (1.8%) cancers. Racial differences were seen with African Americans demonstrating higher IP% of brain metastases compared with other racial groups for most primary sites. IP% was significantly higher for female patients with lung cancer, and significantly higher for male patients with melanoma. The highest IP% of brain metastases occurred at different ages at diagnoses: age 40 to 49 years for primary lung cancer; age 50 to 59 years for primary melanoma, renal, or colorectal cancers; and age 20 to 39 for primary breast cancer. IP% significantly increased as SEER stage of primary cancer advanced for all primary sites., Conclusion: Total IP% of brain metastases was lower than previously reported, and it varied by primary site, race, sex, age at diagnosis of primary cancer, and SEER stage of primary cancer.