Sang Ho Lee1, Koichi Hayano2, Andrew X Zhu3, Dushyant V Sahani2, Hiroyuki Yoshida4. 1. 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St, Suite 400C, Boston, MA 02114. 2. Division of Abdominal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, MA. 3. Massachusetts General Hospital Cancer Center. 4. 3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St, Suite 400C, Boston, MA 02114. Electronic address: yoshida.hiro@mgh.harvard.edu.
Abstract
RATIONALE AND OBJECTIVES: Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data analysis influences its use as a prognostic biomarker. Our aim was to find DCE-MRI parameters that predict 1-year survival (1YS) and overall survival (OS) among patients with advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy by conducting a proof-of-concept comparative study of five different kinetic models. MATERIALS AND METHODS: Twenty patients with advanced HCC underwent DCE-MRI and subsequently received sunitinib. Pretreatment DCE-MRI data were analyzed retrospectively by using the Tofts-Kety (TK), extended TK, two compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. Arterial flow fraction (γ), arterial blood flow (BFA), permeability-surface area product (PS), fractional interstitial volume (vI), and other five parameters were calculated for each model. Individual parameters were evaluated for 1YS prediction using cross-validated Kaplan-Meier analysis, and for association with OS using univariate Cox regression analysis, with additional permutation testing. RESULTS: For 1YS prediction, the TK model-derived γ (P = .007) and vI (P = .029) and the AATH model-derived PS (P = .005) were significant; all these parameters were lower in the high-risk group. Increase in the AATH model-derived PS and the DP model-derived BFA was associated with significant increase in OS with hazard ratios of 0.766 (P = .023) and 0.809 (P = .025), respectively. CONCLUSIONS: The AATH model-derived PS was an effective prognostic biomarker for both 1YS and OS.
RATIONALE AND OBJECTIVES: Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data analysis influences its use as a prognostic biomarker. Our aim was to find DCE-MRI parameters that predict 1-year survival (1YS) and overall survival (OS) among patients with advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy by conducting a proof-of-concept comparative study of five different kinetic models. MATERIALS AND METHODS: Twenty patients with advanced HCC underwent DCE-MRI and subsequently received sunitinib. Pretreatment DCE-MRI data were analyzed retrospectively by using the Tofts-Kety (TK), extended TK, two compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. Arterial flow fraction (γ), arterial blood flow (BFA), permeability-surface area product (PS), fractional interstitial volume (vI), and other five parameters were calculated for each model. Individual parameters were evaluated for 1YS prediction using cross-validated Kaplan-Meier analysis, and for association with OS using univariate Cox regression analysis, with additional permutation testing. RESULTS: For 1YS prediction, the TK model-derived γ (P = .007) and vI (P = .029) and the AATH model-derived PS (P = .005) were significant; all these parameters were lower in the high-risk group. Increase in the AATH model-derived PS and the DP model-derived BFA was associated with significant increase in OS with hazard ratios of 0.766 (P = .023) and 0.809 (P = .025), respectively. CONCLUSIONS: The AATH model-derived PS was an effective prognostic biomarker for both 1YS and OS.
Authors: Sang Ho Lee; Andreas Rimner; Emily Gelb; Joseph O Deasy; Margie A Hunt; John L Humm; Neelam Tyagi Journal: Int J Radiat Oncol Biol Phys Date: 2018-03-02 Impact factor: 7.038