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Signature of survival: a (18)F-FDG PET based whole-liver radiomic analysis predicts survival after (90)Y-TARE for hepatocellular carcinoma
PURPOSE: To generate a predictive whole-liver radiomics scoring system for progression-free survival (PFS) and overall survival (OS) in patients undergoing transarterial radioembolization using Yttrium-90 ((90)Y-TARE) for unresectable hepatocellular carcinoma (uHCC). RESULTS: The generated pPET-RadS...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796994/ https://www.ncbi.nlm.nih.gov/pubmed/29435123 http://dx.doi.org/10.18632/oncotarget.23423 |
Sumario: | PURPOSE: To generate a predictive whole-liver radiomics scoring system for progression-free survival (PFS) and overall survival (OS) in patients undergoing transarterial radioembolization using Yttrium-90 ((90)Y-TARE) for unresectable hepatocellular carcinoma (uHCC). RESULTS: The generated pPET-RadScores were significantly correlated with survival for PFS (median of 11.4 mo [95% confidence interval CI: 6.3–16.5 mo] in low-risk group [PFS-pPET-RadScore < 0.09] vs. 4.0 mo [95% CI: 2.3–5.7 mo] in high-risk group [PFS-pPET-RadScore > 0.09]; P = 0.0004) and OS (median of 20.3 mo [95% CI: 5.7–35 mo] in low-risk group [OS-pPET-RadScore < 0.11] vs. 7.7 mo [95% CI: 6.0–9.5 mo] in high-risk group [OS-pPET-RadScore > 0.11]; P = 0.007). The multivariate analysis confirmed PFS-pPET-RadScore (P = 0.006) and OS-pPET-RadScore (P = 0.001) as independent negative predictors. CONCLUSION: Pretreatment (18)F-FDG PET whole-liver radiomics signature appears as an independent negative predictor for PFS and OS in patients undergoing (90)Y-TARE for uHCC. METHODS: Pretreatment (18)F-FDG PET of 47 consecutive patients undergoing (90)Y-TARE for uHCC (31 resin spheres, 16 glass spheres) were retrospectively analyzed. For each patient, based on PET radiomics signature from whole-liver semi-automatic segmentation, PFS and OS predictive PET-radiomics scores (pPET-RadScores) were obtained using LASSO Cox regression. Using X-tile software, the optimal score to predict PFS (PFS-pPET-RadScore) and OS (OS-pPET-RadScore) served as cutoff to separate high and low-risk patients. Survival curves were estimated using the Kaplan-Meier method. The prognostic value of PFS and OS-pPET-RadScore, Barcelona-Clinic Liver Cancer staging system and serum alpha-fetoprotein level was analyzed to predict PFS and OS in multivariate analysis. |
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