Application evaluation of DCE‑MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer

Ma,Xiang‑Zheng; Lv,Kun; Sheng,Jian‑Liang; Yu,Ying‑Xing; Pang,Pei‑Pei; Xu,Mao‑Sheng; Wang,Shi‑Wei

doi:10.3892/ol.2019.9988

Application evaluation of DCE‑MRI combined with quantitative analysis of DWI for the diagnosis of prostate cancer

Authors:
- Xiang‑Zheng Ma
- Kun Lv
- Jian‑Liang Sheng
- Ying‑Xing Yu
- Pei‑Pei Pang
- Mao‑Sheng Xu
- Shi‑Wei Wang
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Affiliations: Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310006, P.R. China, 2The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang 310006, P.R. China, Department of Life Sciences, GE Healthcare, Shanghai 201203, P.R. China
Published online on: January 29, 2019 https://doi.org/10.3892/ol.2019.9988
Pages: 3077-3084
Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The present study aimed to investigate the value of dynamic contrast‑enhanced magnetic resonance imaging (DCE‑MRI) combined with quantitative analysis of diffusion weighted imaging (DWI) for the diagnosis of prostate cancer (PCa). A total of 81 patients with prostatic diseases, including PCa (n=44) and benign prostatic hyperplasia (BPH, n=37), were imaged with T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), DWI and DCE‑MRI. The blood vessel permeability parameters volume transfer rate constant (Ktrans), back flow rate constant (Kep), extravascular extracellular space volume fraction (Ve), plasma volume fraction (Vp) and apparent diffusion coefficient (ADC) were measured, and compared between the two groups. The efficiency of these tools for the diagnosis of PCa was analyzed by receiver operating characteristic curve analysis. The efficiency of ADC combined with blood vessel permeability parameters in the diagnosis of PCa was analyzed by logistic regression. The correlation between these parameters and the Gleason score was evaluated by Spearman correlation analysis in the PCa group. The results demonstrated that, compared with the BPH group, Ktrans, Kep, Ve and Vp were higher, and ADC was lower in the PCa group (P<0.05). The combination of Kep and ADC offered the highest diagnosis efficiency [area under the curve (AUC=0.939)]. However, the combination of three parameters did not significantly improve the diagnostic efficiency. A subtle improvement in diagnostic efficiency was observed when four parameters (Ktrans + Kep + Ve + ADC) were combined (AUC=0.940), which was significantly higher than with one parameter. The ADC value of the PCa group was negatively correlated with the primary Gleason pattern, secondary Gleason pattern and the total Gleason score in PCa (r=‑0.665, ‑0.456 and ‑0.714, respectively; P<0.001). The Vp in the PCa group was slightly negatively correlated with the primary Gleason pattern of PCa (r=‑0.385; P<0.05); however, no significant correlation was found with secondary Gleason pattern and the total Gleason score. The present study revealed that the combination of DCE‑MRI quantitative analysis and DWI was efficient for PCa diagnosis. This may be because DCE‑MRI and DWI can noninvasively detect water motility in tumor tissues and alterations in permeability during tumor neovascularization. The present study demonstrated that Kep and ADC values may be used as predictive parameters for PCa diagnosis, which may help differentiate benign from malignant prostate lesions.

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