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Our hypothesis is that in vivo MRS from different brain metastases analysed by multivariate analyses distinguish metastases originating from different primary tumors, and also that MR spectra can indicate clinical outcome for these patients.
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Multivariate statistical analyses distinguished tumour from mucosa with a sensitivity of 78.9% and a specificity of 100% (P=3 × 10−7).
Materials and methods: risk factors were determined by univariate Cox regression analysis, and entered in multivariate analyses which distinguished two models: analysis of factors available from history and clinical examination, completed by radiological and surgical data in the second model.
Multivariate analyses were carried out to distinguish the independent effects on certain congeners from those of other covariable congeners.
Fourth, because of the good statistical power, we were able to (1) run multivariate analyses on each gender; (2) distinguish family separation, family discord and perceived parental relationship.
In pancreatic NENs, although grade according to MC was prognostic on univariate analyses, it was not an independent prognostic factor on multivariate analyses and was not able to distinguish G1 from G2 in terms of OS.
Multivariate analyses using 47 morphological measurements clearly distinguished the three treatment groups, i.e. control, VEGF, and Sunitinib.
Uni- and multivariate analyses were used to identify prognostic variables.
Multivariate analyses were performed to identify variables associated with mortality.
Univariate and multivariate analyses were performed to identify clinicopathologic variables associated with poor survival.
Univariate and multivariate analyses were performed to identify predictors for the measured surgical complications.
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