A nomogram that assesses individualized survival probabilities (6-, 12-, and 24-mo) for patients with newly diagnosed GBM could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: http://cancer4.case.edu/rCalculator/rCalculator.html.
Background. Glioblastoma (GBM) is the most common primary malignant brain tumor. Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a nomogram to estimate individualized survival probabilities for patients with newly diagnosed GBM, using data from 2 independent NRG Oncology Radiation Therapy Oncology Group (RTOG) clinical trials. Methods. This analysis included information on 799 (RTOG 0525) and 555 (RTOG 0825) eligible and randomized patients with newly diagnosed GBM and contained the following variables: age at diagnosis, race, gender, Karnofsky performance status (KPS), extent of resection, O 6-methylguanine-DNA methyltransferase (MGMT) methylation status, and survival (in months). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the 0525 data and were independently validated using the 0825 data. Models were internally validated using 10-fold cross-validation, and individually predicted 6-, 12-, and 24-month survival probabilities were generated to measure the predictive accuracy and calibration against the actual survival status. Results. A final nomogram was built using the Cox proportional hazards model. Factors that increased the probability of shorter survival included greater age at diagnosis, male gender, lower KPS, not having total resection, and unmethylated MGMT status. Conclusions. A nomogram that assesses individualized survival probabilities (6-, 12-, and 24-mo) for patients with newly diagnosed GBM could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this nomogram is provided: http:// cancer4.case.edu/rCalculator/rCalculator.html.
to profile 800 human miRNAs in each sample. Univariate (UVA) and multivariate (MVA) Cox regression analyses were performed using progression-free survival (PFS) and overall survival (OS) as clinical endpoints. Results: In our UVA, we identified 10 miRNAs that were significantly associated with PFS (P value < 0.05), however, no miRNAs had a false discover rate (FDR) P value < 0.2. Thirty-nine miRNAs were significantly predictive of OS (P value < 0.05) and 9 had a FDR P value < 0.2. Upon MVA, adjusting for age, grade, and IDH status, 8 miRNAs were found to be significant predictors of progression (P value < 0.05) and 25 miRNAs were significantly predictive of OS (P value < 0.05). FDR P values < 0.2 were not observed in the MVAs. Conclusion: These results provide valuable information that may aid in the development of new and improved classification systems for lower grade glioma, thus changing clinical practice. Additionally, the identification of novel prognostic molecular biomarkers may provide insight to potential therapeutic strategies combating disease progression. Future work will focus on validating these miRNAs in additional glioma cohorts and in preclinical models as well as identifying molecular biomarkers predictive of treatment response.
The results suggest that there may be a survival advantage for patients with colon and rectal cancers being treated within a specialist colorectal surgical unit.
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