Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma
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Paul Blanc-Durand1,*, Axel Van Der Gucht1,*, Mario Jreige1, Marie Nicod-Lalonde1, Marina Silva-Monteiro1, John O. Prior1, Alban Denys2, Adrien Depeursinge3,* and Niklaus Schaefer1,*
1Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland
2Department of Radiology and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland
3Institute of Information Systems, University of Applied Sciences Western Switzerland [HES-SO], Sierre, Switzerland
*These authors contributed equally to this work
Paul Blanc-Durand, email: [email protected]
Keywords: 18F-FDG PET; TARE; radiomics; hepatocellular carcinoma; survival
Received: June 15, 2017 Accepted: November 28, 2017 Published: December 19, 2017
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 (90Y-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 18F-FDG PET whole-liver radiomics signature appears as an independent negative predictor for PFS and OS in patients undergoing 90Y-TARE for uHCC.
Methods: Pretreatment 18F-FDG PET of 47 consecutive patients undergoing 90Y-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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