Differentiating False Positive Lesions from Clinically Significant Cancer and Normal Prostate Tissue Using VERDICT MRI and Other Diffusion Models

Abstract

False positives on multiparametric MRIs (mp-MRIs) result in many unnecessary invasive biopsies in men with clinically insignificant diseases. This study investigated whether quantitative diffusion MRI could differentiate between false positives, true positives and normal tissue non-invasively. Thirty-eight patients underwent mp-MRI and Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI, followed by transperineal biopsy. The patients were categorized into two groups following biopsy: (1) significant cancer—true positive, 19 patients; (2) atrophy/inflammation/high-grade prostatic intraepithelial neoplasia (PIN)—false positive, 19 patients. The clinical apparent diffusion coefficient (ADC) values were obtained, and the intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and VERDICT models were fitted via deep learning. Significant differences (p < 0.05) between true positive and false positive lesions were found in ADC, IVIM perfusion fraction (f) and diffusivity (D), DKI diffusivity (DK) (p < 0.0001) and kurtosis (K) and VERDICT intracellular volume fraction (fIC), extracellular−extravascular volume fraction (fEES) and diffusivity (dEES) values. Significant differences between false positives and normal tissue were found for the VERDICT fIC (p = 0.004) and IVIM D. These results demonstrate that model-based diffusion MRI could reduce unnecessary biopsies occurring due to false positive prostate lesions and shows promising sensitivity to benign diseases.

Keywords: biophysical modeling; deep learning; diffusion MRI; false positives; prostate cancer.

Figure 1

Flowcharts describing full methodology of study. (a) Image acquisition pipeline to determine nature of patient lesion. (b) Image analysis pipeline for deep learning parameter estimation used to fit IVIM, DKI and VERDICT models.

Figure 2

Boxplots showing the parameter estimates obtained using the ADC, DKI, IVIM and VERDICT models. We observed significant differences between true and false positives in the ADC d (p = 0.002); IVIM D (p = 0.01) and f (p = 0.0002); DKI D_K (p < 0.0001) and K (p = 0.0001); and VERDICT f_IC (p = 0.001), f_EES (p = 0.002) and d_EES (p = 0.0004). The d, D_K, D, f, f_EES and d_EES are all lower in true positives than false positives, while K and f_IC are higher. We also found statistically significant differences between false positives and normal tissue for the VERDICT f_IC (p = 0.004), with higher values in false positive lesions than in normal tissue, and the IVIM D (p = 0.02) with lower values in false positive lesions than in normal tissue. Outliers are denoted by a circle and asterisks indicate statistical significance.

Figure 3

Parametric maps obtained using the IVIM, DKI and VERDICT models in a 70-year-old patient with a false positive lesion and a 72-year-old patient with a true positive lesion. Only the parameters which successfully differentiated between the two lesion types are included, as well as the clinical ADC maps. We observed that the VERDICT maps highlight the true positive lesion most conspicuously, and the VERDICT f_IC also distinguishes the false positive lesion from the surrounding tissue.

Figure 4

ROC curves for ADC, DKI, IVIM and VERDICT parameters—those on the left are for discriminating true (TP) and false positives (FP), and those on the right are for discriminating false positives and normal tissue (NT). We observed that the largest AUC for discrimination between true and false positives is achieved by the DKI D_K (AUC = 0.9086). The largest AUC for discrimination between false positives and normal tissue is achieved by the IVIM D (AUC = 0.7036), closely followed by the VERDICT f_IC (AUC = 0.6981).

Figure 5

Scatter plots showing the correlation between VERDICT parameters and the DKI and IVIM parameters. For D_K and D, we observed a negative correlation with f_IC and positive correlation with f_EES and d_EES. K shows a positive correlation with f_IC and negative correlation with f_EES. Finally, D* shows a negative correlation with f_EES and positive correlation with f_VASC.