Related Experiment Video
Updated: May 15, 2026

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
Published on: April 18, 2015
Pharmacokinetic parameters derived from dynamic contrast enhanced MRI of cervical cancers predict chemoradiotherapy
Erlend K F Andersen1, Knut Håkon Hole, Kjersti V Lund
1Department of Medical Physics, Oslo University Hospital, Olso, Norway.
Purpose:
To assess the prognostic value of pharmacokinetic parameters derived from pre-chemoradiotherapy dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of cervical cancer patients.
Materials And Methods:
Seventy-eight patients with locally advanced cervical cancer underwent DCE-MRI with Gd-DTPA before chemoradiotherapy. The pharmacokinetic Brix and Tofts models were fitted to contrast enhancement curves in all tumor voxels, providing histograms of several pharmacokinetic parameters (Brix: A(Brix), k(ep), k(el), Tofts: K(trans), ν(e)). A percentile screening approach including log-rank survival tests was undertaken to identify the clinically most relevant part of the intratumoral parameter distribution. Clinical endpoints were progression-free survival (PFS) and locoregional control (LRC). Multivariate analysis including FIGO stage and tumor volume was used to assess the prognostic significance of the imaging parameters.
Results:
A(Brix), k(el), and K(trans) were significantly (P<0.05) positively associated with both clinical LRC and PFS, while ν(e) was significantly positively correlated with PFS only. k(ep) showed no association with any endpoint. A(Brix) was positively correlated with K(trans) and ν(e), and showed the strongest association with endpoint in the log-rank testing. k(el) and K(trans) were independent prognostic factors in multivariate analysis with LRC as endpoint.
Conclusions:
Parameters estimated by pharmacokinetic analysis of DCE-MR images obtained prior to chemoradiotherapy may be used for identifying patients at risk of treatment failure.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
15:48Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014