Dynamic contrast-enhanced case-control analysis in 3T MRI of prostate cancer can help to characterize tumor aggressiveness
Introduction
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an interesting technique to non-invasively evaluate prostate cancer and establish perfusion differences between tumor grade and normal tissue [1]. It has been included in the qualitative assessment of the malignancy probability of a certain region through the PI-RADS scoring system [2], [3]. Currently, in version 2, the role of DCE-MRI is secondary to diffusion imaging and it focuses on subjectively assessing the enhancement characteristics of the peripheral zone.
Quantitative parameters obtained from pharmacokinetic models [4], [5] applied to DCE-MRI series have demonstrated superior performance to differentiate cancerous from normal tissue prostate in comparison to simpler enhancement analysis such as semi-quantitative or qualitative parameters [6], [7], as they provide physiological information related to capillary permeability, blood fraction and interstitial space volume.
Several research groups have published pharmacokinetic parameters results both from the normal and pathological prostate gland (Table 1). There are striking differences in the results across the studies, with overlaps between normal (0.36 ± 0.60, mean ± standard deviation) and carcinoma (0.55 ± 0.59) Ktrans values. Strict comparisons among results are limited due to variability in methodologies. Notwithstanding, at least four main sources of variability can be identified: MRI acquisition protocol, type of gadolinium-based contrast agent, signal analysis methods and statistical approaches.
The variability in image analysis methods also relates to the selection of the arterial input function (AIF) (individual manual, individual automatic, population-averaged or reference); the selection of the pharmacokinetic model; the selection of the region of interest (whole prostate, only the hot spots or only central/peripheral gland) and the signal intensity to contrast agent concentration conversion strategy. The measured properties can also be analyzed on a voxel-by-voxel basis or after averaging the enhancement curve over manually defined ROIs. The statistical approach has also methodological differences, such as the use of different statistical descriptors either from the whole ROI or from some percentile of the data distribution. Standards need to be reached with regard to image and series acquisition, and data processing [23], [24], [25], a fact that may limit a widespread clinical use and multicenter comparisons of these parameters.
The aim of this study was to establish prospectively ranges of normality and tumor-specific values for the standardized DCE-MRI pharmacokinetic parameters of the prostate at 3T, and to compare the performance of the quantitative and semi-quantitative parameters. A secondary aim was to give further evidence of the potential of the pharmacokinetic parameters as imaging biomarkers. Recommendations proposed by the QIBA group [26] have been followed wherever possible.
Section snippets
Subjects
The Ethics Committee approved this study. All patients signed an informed consent for the inclusion of their anonymized data in the study.
The initial group consisted of 133 eligible patients. The inclusion criterion for the tumor group was histological confirmation of prostate cancer. The inclusion criteria for the healthy group were asymptomatic patients; stable PSA (<2.5 ng/ml) in at least two controls; negative digital rectal examination; no significant increase in PSA and negative digital
Comparison between peripheral healthy and tumor ROIs
Ktrans, ve, upslope and AUC60 showed statistically significant differences for all the statistical descriptors. Table 3 shows a summary of the results.
The contingency table for the curve type showed no significant association between healthy/tumor peripheral areas and the three types of curve (chi-square = 0.702, AUROC curve 0.53). Healthy peripheral areas showed 12%, 69% and 19% of curve types I, II and III, respectively. Tumor areas showed 11%, 63% and 26% of curves I, II and III, respectively.
Quantitative analysis
The values for Ktrans were in the range of other studies [8], [10], [11], [17], [20], [22], with mean values of 0.2 min−1 for the healthy peripheral gland and 0.4 min−1 for the tumor. Franiel et al. [13] and Vos et al. [18] obtained slightly higher values for both regions. Other works reported comparatively lower values for both regions [14], [16], [19], [21]. On the other side, Lüdemann et al. [9] reported much higher values.
For kep, no significant differences between healthy and tumor
Conclusions
The results presented in this study contribute to the standardization of tumor and normal prostate values for quantitative parameters obtained from prostate DCE-MR images. It has been demonstrated that quantitative (Ktrans and ve) and semi-quantitative (upslope and AUC60) perfusion parameters show statistically significant differences between tumors and control areas in the peripheral prostate. However, the overall performance of individual DCE-MRI parameters remains relatively poor to
Conflict of interest
The authors declare no conflict of interest.
Funding
This work was supported by a grant from GUERBET (investigator-initiated study). GUERBET did not have any role in the study design, implementation and discussions on the study results.
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2019, Canadian Association of Radiologists JournalCitation Excerpt :The Ktrans for low-grade tumours reported in their protocol was also similar to ours (0.56 ± 0.12), but for high-grade tumours, the Ktrans value (0.60 ± 0.17) was lower than that of the present study. Sanz-Requina et al [11] studied a similar small group and reported no clinically significant differences between GS of 6 and GS of 7 for Ktrans, Kep, or Ve. Similar to the studies described above, Oto et al [10] reported no significant correlation between GS and quantitative perfusion parameters and did not clarify the mean or median values for the subgroups.
Can DCE-MRI reduce the number of PI-RADS v.2 false positive findings? Role of quantitative pharmacokinetic parameters in prostate lesions characterization
2019, European Journal of RadiologyCitation Excerpt :Nevertheless, these results could be influenced by the great numeric difference among our GS classes, with GS 7 representing 74% of all neoplastic findings. Cho E et al. [25] and Sanz-requena R et al. [27] in their studies proposed a Ktrans cut-off value for the prediction of significant PCa (184 × 10−3/min and 210 × 10−3/min, respectively); these results are highly comparable with our findings (Ktrans cut-off: 191 × 10−3/min). In our study we were unable to find a satisfying cut-off for Kep values.
Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences
2019, Medical Image AnalysisCitation Excerpt :DCE (dynamic contrast enhanced) imaging using computed tomography (CT), magnetic resonance imaging (MRI) or ultrasound imaging (US) appears promising as it can monitor the local changes in microcirculation secondary to the development of new vessels (neo-angiogenesis). DCE-MRI and DCE-CT (called also CT-perfusion) have been extensively tested alone or in combination with other techniques (Winfield et al., 2016) in pathological conditions such as cancer, ischemia and inflammation, in various tissues including brain (Bergamino et al., 2014), breast (Chen et al., 2010), prostate (Sanz-Requena et al., 2016), heart (Bakir et al., 2016; Nee et al., 2009), kidney (Woodard et al., 2015), liver (Raj and Juluru, 2009; Chen and Shih, 2014), genital organs, gastrointestinal tract, bone (Michoux et al., 2012) and placenta (Frias et al., 2015). They show a great potential to: 1/ detect and characterize lesions (Sanz-Requena et al., 2016; Ferre et al., 2016; Bhooshan et al., 2010); 2/ personalize treatment including new targeted drugs, radiotherapy and mini invasive surgery; 3/ monitor and optimize treatments during the follow-up (Padhani and Khan, 2010; Wang et al., 2014) or after heart, liver or kidney transplants (Khalifa et al., 2013).