Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis.

Background: The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis.

Materials and methods: This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles.

Results: Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model's curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%-91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%-97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%-91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%-90.91%], perfusion-related diffusion [D*] 65% [48.47%-81.53%] and perfusion fraction [f] 80.36% [64.23%-96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92-0.97), for DKI parameters (DK 0.94 [0.89-0.99] and K 0.93 [0.90-0.96]) and for IVIM parameters (DIVIM 0.85 [0.80-0.91], D* 0.60 [0.43-0.77] and f 0.73 [0.63-0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM-kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%-93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%-79.48%), respectively.

Conclusion: Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.