Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

BACKGROUND: The accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear. PURPOSE: To compare the whole solid-tumor volume histogram analysis of diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE-MRI. STUDY TYPE: Retrospective. POPULATION: Twenty patients with BEOTs and 42 patients with MEOTs. FIELD STRENGTH/SEQUENCE: 1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE-MRI. ASSESSMENT: Histogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (K(trans) ), rate constant (kep ), and extravascular extracellular volume fraction (ve ). STATISTICAL TESTS: The Mann-Whitney U-test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE-MRI parameters. RESULTS: ADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P < 0.05). K, K(trans) , kep , and ve were significantly lower in BEOTs than in MEOTs (P < 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with K(trans) yielded the highest AUC of 0.950. A weak inverse correlation was found between D and K(trans) (r = -0.320, P = 0.025) and between D and kep (r = -0.267, P = 0.037). DATA CONCLUSION: The 10th percentile of Dk was the most valuable metric and Dk combined with K(trans) had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion-related parameters derived from IVIM and DCE-MRI. LEVEL OF EVIDENCE: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020.