Tracer kinetic model selection for dynamic contrast-enhanced computed tomography imaging of prostate cancer - Abstract

Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.


To investigate the conditions under which the Tofts, extended Tofts, and adiabatic approximation to the tissue homogeneity (AATH) model are the optimal tracer kinetic models (TKMs) for the quantification of dynamic contrast-enhanced (DCE) computed tomography (CT) examinations in prostate cancer.

This prospective study was approved by the local research ethics committee, and all patients gave written informed consent. A total of 29 patients (mean age, 69.1 years; range, 56-80 years) with biopsy-proven prostate cancer underwent a DCE-CT examination prior to radiation therapy. TKM parameter maps were calculated for each patient with the Tofts, extended Tofts, and AATH models. For each voxel, corrected Akaike information criterion values were calculated, taking into account both the goodness-of-fit and the number of model parameters. We consider the optimal model as the model with the lowest corrected Akaike information criterion.

All 3 TKMs are the optimal models in part of the prostate. For individual patients, the AATH model was the optimal model in 25.0% to 88.9%, the Tofts in 2.7% to 71.8%, and the extended Tofts model in 0.7% to 68.7% of the prostate voxels. The Tofts model was optimal in low flow regions (< 0.1 min), the extended Tofts model in regions with high flow (>0.4 min) and low transit time (< 12 seconds), and the AATH model in the intermediate flow range (0.1-0.4 min). However, differences between the 3 models were small and TKM parameter estimates gave consistent results between the 3 models.

All the 3 models gave reasonable fits of DCE-CT data from the prostate. In view of the small parameter range in which the Tofts and extended Tofts models outperform the AATH model, the latter seems the optimal model for quantification of DCE-CT data of the prostate.

Written by:
Korporaal JG, van Vulpen M, van den Berg CA, van der Heide UA.   Are you the author?

Reference: Invest Radiol. 2011 May 23. Epub ahead of print.

PubMed Abstract
PMID: 21610505 Prostate Cancer Section



email news signup