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Advances in molecular biology are providing better prognostic and predictive markers that can help in designing and predicting response to treatment of CNS tumors.
Despite these advances, the need for better prognostic and predictive markers persists, as heterogeneity in patient outcome exists within subgroups.
Of these, nearly 80% develop local or distant recurrence within 2 years, reflecting the need for better prognostic and predictive biomarkers to dictate adjuvant therapy [ 4].
Recently, gene expression analysis was shown to elucidate the biological variance between tumors and molecular markers were identified that led to new classification systems that provided better prognostic and predictive parameters.
All patients had grade 2 (G2) tumors, belonging to a large heterogeneous group of patients with respect to the other grades, which would benefit from better prognostic and predictive factors [ 3].
Approaches including total glycolytic volume [63], texture features (descriptive measures of tracer uptake heterogeneity) [17,36] and even the simple method of combining tumour diameter with SUVmax to produce a 'diameter-SUV index' [31] may offer better predictive and prognostic utility.
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The FDA defines 4 categories of clinical biomarkers: diagnostic, prognostic, predictive, and pharmacodynamic.
Prognostic, predictive, and biological roles have also been described for miRNAs in NPC, as discussed below.
HMGCR expression in breast tumors has recently been proposed to hold prognostic and treatment-predictive information.
Such miRNA signatures may have better diagnostic and prognostic value.
Concerning predictive performance, we found the statistically derived model to be slightly better than the expert-based prognostic model.
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