RESUMO
PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679±0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.
Assuntos
Biomarcadores Tumorais/genética , Neoplasias Encefálicas/diagnóstico , Neoplasias Encefálicas/mortalidade , Glioblastoma/diagnóstico , Glioblastoma/mortalidade , Imageamento por Ressonância Magnética/métodos , Neoplasias Encefálicas/genética , Feminino , Marcadores Genéticos/genética , Predisposição Genética para Doença/epidemiologia , Predisposição Genética para Doença/genética , Glioblastoma/genética , Humanos , Masculino , Prevalência , Reprodutibilidade dos Testes , Estudos Retrospectivos , Medição de Risco/métodos , Sensibilidade e Especificidade , Análise de SobrevidaRESUMO
The purpose of this study was to assess the usefulness of diffusion weighted imaging as an additional imaging biomarker for treatment response in recurrent/progressive malignant gliomas treated with bevacizumab alone or in combination with other chemotherapeutic agents. Twenty patients treated with bevacizumab alone or concurrent chemotherapy were followed up with serial MR imaging. Volume and ADC values of contrast enhancing lesion (CEL(vol), CEL(ADC)) and also of non-enhancing lesion (NEL(vol), NEL(ADC)) were obtained. CEL(vol) showed a progressive decrease in non-progressors with a median percentage change of -73.2% (P = 0.001) as compared to -33.4% for progressors by 1 year/last imaging (P = 0.382). NEL(vol) also showed a decrease in non-progressors on follow up imaging though only significant for 3 months follow up (P = 0.042). In progressors, CEL(vol) and NEL(vol) showed initial decrease followed by slight increase by 1 year/last imaging though not significant (P value of 0.382 and 0.46, respectively). CEL(ADC) and NEL(ADC) in non-progressors did not show any statistically significant change though there was slight trend for positive percent change especially for CEL(ADC) by 1 year/last imaging follow up study (P value of 0.077 and 0.339, respectively). Progressors showed a progressive negative percent change of CEL(ADC) and NEL(ADC). In progressors, NEL(ADC) decreased at 6 weeks (P = 0.054), 3 months (P = 0.023) and 1 year/last (P = 0.078) as compared to baseline study and was also statistically significant as compared to non-progressors at 6 weeks (P = 0.047) and 3 months (P = 0.025). CEL(ADC) and NEL(ADC) appear to follow different trends over time for non-progressors and progressors with a stable to slightly progressive increase in non-progressors and a progressive decrease in progressors, especially early on. These findings suggest that DWI may be used as an additional imaging biomarker for early treatment response.