Evaluating Breast Cancer Intravoxel Incoherent Motion MRI Biomarkers across Software Platforms

Purpose To evaluate intravoxel incoherent motion (IVIM) biomarkers across different MRI vendors and software programs for breast cancer characterization in a two-site study. Materials and Methods This institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study included 106 patients (with 18 benign and 88 malignant lesions) who underwent bilateral diffusion-weighted imaging (DWI) between February 2009 and March 2013. DWI was performed using 1.5-T (<i>n</i> = 6) or 3-T MRI scanners from two vendors using single-shot spin-echo echo-planar imaging or twice-refocused, bipolar gradient single-shot turbo spin-echo readout with multiple <i>b</i> values between 0 and 1000 sec/mm². IVIM parameters tissue diffusivity (<i>D_t</i>), perfusion fraction (<i>f_p</i>), pseudo-diffusivity (<i>D_p</i>), and their respective first-order radiomics were derived using two software packages (Igor; Wavemetrics, and Firevoxel; New York University). Bland-Altman analysis compared IVIM metrics from the two software programs. Histopathology was the reference standard, where logistic regressions with adjustments for site compared benign and malignant lesions. Least absolute shrinkage and selection operator (LASSO) penalized multivariable regression was performed first for metrics derived from each parameter separately, and then after incorporating metrics from all three parameters. Area under receiver operating characteristic (ROC) curve (AUC) ± standard error was used to quantify the diagnostic value. Performance was also evaluated using threefold cross-validation of the combined cohort. Results In total, 49 (mean age, 49 years ± 11 [SD]) and 57 (mean age, 48 years ± 10) female patients were enrolled from sites 1 and 2, respectively. Software 1 (Igor) and software 2 (Firevoxel) identified diagnostic biomarkers individually and in multivariable analysis. Tissue diffusivity exhibited the highest software consistency, with coefficients of variation of 4.8% and 2.8% (site 1 and site 2, respectively), followed by perfusion fraction (14.5% and 18.9%) and pseudo-diffusivity (36.9% and 19.8%). The highest performing metrics were <i>D_t,min</i> (AUC, 0.786 ± 0.05), <i>f_p,max</i> (AUC, 0.835 ± 0.04), and <i>D_p,max</i> (AUC, 0.804 ± 0.05) for software 1 and <i>D_t,skew</i> (AUC, 0.82 ± 0.05), <i>f_p,max</i> (AUC, 0.82 ± 0.046), and <i>D_p,max</i> (AUC, 0.75 ± 0.06) for software 2. Five metrics (<i>D_t,min, D_t,skew, f_p,max, D_p,min, D_p,max</i>) were included in the multivariable regression, achieving AUCs of 0.90 ± 0.03 and 0.90 ± 0.03 for software 1, and 0.84 ± 0.04 and 0.81 ± 0.05 for software 2, without and with cross-validation, respectively. Conclusion This study confirmed the translational potential of IVIM biomarkers for breast cancer characterization. <b>Keywords:</b> MR-Diffusion Weighted Imaging, Breast, Technology Assessment <i>Supplemental material is available for this article.</i> © RSNA, 2025.