title: Simple models improve the discrimination of prostate cancers from the peripheral gland by T1-weighted dynamic MRI.
abstract: Evaluation of the accuracy of descriptive and physiological parameters calculated from signal intensity-time curves using T1-weighted dynamic contrast enhanced MRI (DCE MRI) to differentiate prostate cancers from the peripheral gland. Twenty-seven patients with prostate cancers were examined with DCE MRI prior radical prostatectomy. Regions of interest were defined in tumors and non-affected areas in the peripheral zone. Dynamic data were parameterized in amplitude and exchange rate constant (kep) using a two-compartment model. Additionally, relative slope during 26, 39, 52 and 65 s, areas under the curve (AUC) and time to start of signal intensity increase (tlag) were determined. Vessel density (VD) of excised prostates was quantified in tumor areas using a CD34 stain. The parameter slope52 showed 20% higher values (P<0.001) in tumors than in the peripheral gland and compared with the other parameters the largest area under the ROC curve (0.81). The minimum total error rate was attained at a cut-point of 0.021, yielding a sample value of sensitivity and specificity of 70% and 88%, respectively, and a bias-corrected sum of sensitivity and specificity of 1.54. In addition, amplitude (P<0.001), kep (P=0.03) and AUC (P<0.001) were significantly higher in tumors. tlag did not discriminate carcinomas from glandular tissue. VD was higher in tumors than in the non-affected peripheral prostate (P=0.05). However, none of the dynamic parameters in carcinomas showed a significant correlation with VD or Gleason score. Although pharmacokinetic modeling in DCE MRI showed potential to discriminate prostate cancers from peripheral prostate tissue, descriptive parameters of the early signal enhancement after contrast media injection reached higher sensitivity and specificity.
authors.affiliation: Department of Medical Physics in Radiology, Deutsches Krebsforschungszentrum, INF 280, Heidelberg, Germany. f.kiessling@dkfz.de
authors.forename: Fabian
authors.initials: F
authors.lastname: Kiessling
authors.forename: Matthias
authors.initials: M
authors.lastname: Lichy
authors.forename: Rainer
authors.initials: R
authors.lastname: Grobholz
authors.forename: Melanie
authors.initials: M
authors.lastname: Heilmann
authors.forename: Nabeel
authors.initials: N
authors.lastname: Farhan
authors.forename: Maurice Stephan
authors.initials: MS
authors.lastname: Michel
authors.forename: Lutz
authors.initials: L
authors.lastname: Trojan
authors.forename: Joerg
authors.initials: J
authors.lastname: Ederle
authors.forename: Ulrich
authors.initials: U
authors.lastname: Abel
authors.forename: Hans-Ulrich
authors.initials: HU
authors.lastname: Kauczor
authors.forename: Wolfhard
authors.initials: W
authors.lastname: Semmler
authors.forename: Stefan
authors.initials: S
authors.lastname: Delorme
mesh_terms: D000368:Aged; D018952:Antigens, CD34; D019540:Area Under Curve; D002277:Carcinoma / Q000098:blood supply / Q000175:diagnosis; D004396:Coloring Agents; D003287:Contrast Media; D019786:Gadolinium DTPA; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D008297:Male; D008833:Microcirculation / Q000473:pathology; D008875:Middle Aged; D008954:Models, Biological; D011467:Prostate / Q000098:blood supply / Q000473:pathology*; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000098:blood supply / Q000175:diagnosis*; D012372:ROC Curve; D012680:Sensitivity and Specificity
chemical_list: D018952:Antigens, CD34; D004396:Coloring Agents; D003287:Contrast Media; D019786:Gadolinium DTPA