Signature of survival: a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma

// Paul Blanc-Durand 1, * , Axel Van Der Gucht 1, * , Mario Jreige 1 , Marie Nicod-Lalonde 1 , Marina Silva-Monteiro 1 , John O. Prior 1 , Alban Denys 2 , Adrien Depeursinge 3, * and Niklaus Schaefer 1, * 1 Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland 2 Department of Radiology and Interventional Radiology, Lausanne University Hospital, Lausanne, Switzerland 3 Institute of Information Systems, University of Applied Sciences Western Switzerland HES-SO, Sierre, Switzerland * These authors contributed equally to this work Correspondence to: Paul Blanc-Durand, email: paul.blancdurand@gmail.com Keywords: 18 F-FDG PET; TARE; radiomics; hepatocellular carcinoma; survival Received: June 15, 2017      Accepted: November 28, 2017      Published: December 19, 2017 ABSTRACT 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; 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; 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.