Paper Info
Title
Reproducibility Of Computed Tomography Perfusion Parameters In Hepatic Multicentre Study In Patients With Colorectal Cancer
Abstract
Objective: The Computed Tomography perfusion (CTp) is a promising tool in oncology to characterize tissue hemodynamics, but the difficulty to achieve reproducible perfusion parameters in several organs, with different methods, contributes to hamper the clinical translation of CTp. The goal of this study is to set up a new approach aiming at achieving multicentre reproducibility of blood flow (BF) values in liver.Methods: 75 patients from two Centres (A and B) underwent an axial liver CTp, including arterial and portal phases. A dedicated workflow addressing modelling and computational aspects was implemented, including a novel two-stage strategy to separate the dual-input contributions of hepatic signals, thus allowing to compute independently both Maximum Slope (MS) and Deconvolution (DV) on the same contributing signals.Results: 95% of patients in A and B showed an excellent voxel-based Pearson correlation (rho >= 0.96) between MS and DV BF values, with very low coefficients of variation (CV = 0.11 in the worst case). The good concordance is confirmed for the whole cohorts, in single Centres and both, where R-2 =0.97, rho >= 0.97, rho(s) >= 0.96, ICC >= 0.78 and CV=0.25 are the worst values. Compared with eighteen recent articles, these represent by far the best outcomes.Conclusion: The excellent patient- and cohort-based reproducibility of BF values achieved independently by MS and BV confirms the effectiveness of the approach presented.Significance: Our approach can be used to improve the reproducibility in other CTp multicentre studies, in liver as well as in other organs, with even different clinical questions, and represents a marked step forward towards CTp standardization, favouring the investigation of imaging biomarkers.
Year
DOI
Venue
2021
10.1016/j.bspc.2020.102298
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
Signal processing, Deconvolution, Computed tomography, Oncology, Reproducibility
Journal
64
ISSN
Citations 
PageRank 
1746-8094
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Margherita Mottola100.34
Alessandro Bevilacqua220026.45