Rationale and Objectives
To test whether individually measured arterial input function (AIF) provides more accurate prostate cancer diagnosis then population average AIF when dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data are acquired with limited temporal resolution.
Materials and Methods
Twenty-six patients with a high clinical suspicion for prostate caner and no prior treatment underwent DCE MRI examination at 3.0 T before biopsy. DCE MRI data were fitted to a pharmacokinetic model using three forms of AIF: an individually measured, a local population average, and a literature double exponential population average. Receiver operating characteristic (ROC) analysis was used to correlate MRI with the biopsy results. Goodness of fit (χ 2 ) for the three AIFs was compared using nonparametric Mann-Whitney test.
Results
Average volume transfer constant (K trans ) values were significantly higher in tumor than in normal peripheral zone for all three AIFs. The individually measured and the local population average AIFs had the highest sensitivity (76%), whereas the double exponential AIF had the highest specificity (82%). The areas under the ROC curves were not significantly different between any of the AIFs (0.81, 0.76, and 0.81 for the individually measured, local population average, and double exponential AIFs, respectively). χ 2 was not significantly different for the three AIFs; however, it was significantly higher in enhancing than in nonenhancing regions for all three AIFs.
Conclusions
These results suggest that, when DCE MRI data are acquired with limited temporal resolution, experimentally measured individual AIF is not significantly better than population average AIF in predicting the biopsy results in prostate cancer.
Magnetic resonance imaging (MRI) has been used in prostate cancer diagnosis with varying success for more than 20 years . In particular, dynamic contrast enhanced MRI (DCE MRI) is one of the techniques that have shown the potential to provide accurate tumor detection and delineation . In addition, quantitative analysis of T 1 -weighted DCE MRI has been used to study the vascular characteristics of the prostate cancer and their changes after neoadjuvant therapy .
Studies have shown that cancers, principally in the peripheral zone of the prostate gland, enhance more rapidly than normal tissues after administration of a low molecular weight contrast agent . The mechanism of differentiating tumors from normal prostatic tissue with DCE MRI is not entirely clear. Several researchers suggested that the microvessel density plays a decisive role in this mechanism , because the contrast agent uptake in the tissue is dependent on the microvessel density , and microvessel density is a recognized prognostic factor for prostate cancer .
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Materials and methods
Patient Selection and Biopsy
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MRI Examinations
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Data Processing
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Arterial Input Function
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Statistical Data Analysis
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Results
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Table 1
Average Values (Mean ± Standard Deviation) of DCE MRI Parameters
PCa PZ K trans (min −1 ) v e v p K trans (min −1 ) v e v p PS 0.16 ± 0.06 ∗ 0.22 ± 0.05 0.01 ± 0.02 0.08 ± 0.06 0.23 ± 0.01 0.02 ± 0.02 LG 0.15 ± 0.07 ∗ 0.20 ± 0.05 0.01 ± 0.02 † 0.09 ± 0.07 0.23 ± 0.10 0.02 ± 0.02 DE 1.86 ± 1.19 ∗ 0.31 ± 0.08 0.02 ± 0.04 ‡ 0.61 ± 0.83 0.30 ± 0.11 0.04 ± 0.03
DCE, dynamic contrast enhanced; MRI, magnetic resonance imaging; PS, patient-specific; LG, local Gaussian; DE, double exponential population average; K trans , volume transfer constant; v e , fractional volume of the extravascular extracellular space; v p , fractional plasma volume; PCa, prostatic adenocarcinoma ( n = 29); PZ, normal peripheral zone ( n = 213).
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Table 2
Performance Measures for the Three AIFs
PS LG DE Sensitivity 76% (22/29) (68%–82%) 76% (22/29) (68%–82%) 65% (19/29) (58%–73%) Specificity 77% (167/217) (70%–83%) 76% (164/217) (68%–82%) 82% (177/217) (75%–87%) PPV 31% (22/72) (24%–38%) 29% (22/75) (23%–37%) 32% (19/59) (25%–40%) NPV 96% (167/174) (91%–98%) 96% (164/171) (91%–98%) 95% (177/187) (90%–97%) Accuracy 77% (189/246) (69%– 3%) 76% (186/246) (69%–83%) 80% (196/246) (73%–85%)
AIF, arterial input functions; PS, patient-specific AIF; LG, average of individual measured AIFs fitted to double Gaussian plus exponential function; DE, population average double exponential AIF; PPV, positive predictive value; NPV, negative predictive value.
95% confidence intervals are provided in parentheses.
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Table 3
The Median Values of the Goodness of Fit (χ 2 ) for the Three AIFs
PS LG DE TP 0.2962 (0.1070–0.9060) 0.2470 (0.0795–0.7537) 0.2493 (0.0903–1.4474) TN 0.1764 (0.0390–1.2353) 0.1609 (0.0391–1.2426) 0.1539 (0.0337–1.2359) FP 0.2106 (0.0832–0.9487) 0.2216 (0.0562–0.9952) 0.1823 (0.0395–1.0267) FN 0.1073 (0.0691–0.2736) 0.0750 (0.0529–0.1361) 0.0753 (0.0367–0.5300)
TP, true positives (enhancing regions with positive biopsy); TN, true negatives (nonenhancing regions with negative biopsies); FP, false positives (enhancing regions with negative biopsies); FN, false negatives (nonenhancing regions with positive biopsies); PS, patient-specific AIF; LG, average of individual measured AIFs fitted to double Gaussian plus exponential function; DE, population average double exponential AIF.
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Discussion
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