Histogram Analysis of Diffusion-weighted Imaging with a Fractional Order Calculus Model in Breast Cancer: Diagnostic Performance and Associations with Prognostic Factors

BO HU¹CAILI TANG²QILAN HU²XU YAN³TAO AI^2*

• ¹Radiology, Yichang Central People's Hospital, Yichang, China
• ²Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
• ³Scientific Marketing, Siemens Healthineers, SHanghai, China

Objective: To evaluate the diagnostic performance of diffusion-weighted imaging (DWI) with a fractional order calculus (FROC) model for differentiating breast lesions, and to explore the associations between FROC/ADC-derived diffusion metrics and prognostic biomarkers and molecular subtypes in breast cancer. Methods: This retrospective study included 147 patients with 159 histopathology-confirmed lesions who underwent multi-b DWI using simultaneous multi-slice (SMS) readout-segmented echo-planar imaging (rs-EPI) at 3.0T. Whole-lesion histograms were computed for mono-exponential ADC and FROC parameters (D, β, μ). The Mann-Whitney U test was used to compare the histogram metrics of each diffusion parameter between the benign and malignant groups, as well as between groups with different prognostic biomarkers, and molecular subtypes. The Kruskal-Wallis test was used to compare the histogram metrics of each DWI-derived parameter among the different molecular subtypes. The Spearman rank correlation analysis was employed to characterize correlations between diffusion parameters and prognostic biomarkers. The diagnostic performance of each DWI-derived parameter in differentiating breast lesions was assessed using receiver operating characteristic (ROC) analysis. Results: Interobserver reproducibility was excellent (ICCs 0.827–0.928). Central-tendency histogram metrics (10th, 90th percentiles, mean, median) of ADC and FROC parameters were higher in benign than malignant lesions, whereas skewness (all models) and entropy/kurtosis (ADC, D, μ) were lower in benign lesions (all p < 0.05, except β-skewness). The histogram metrics of ADC-median, DFROC-mean, and DFROC-median showed similar diagnostic performance. The values of ADC-mean, DFROC-10%, DFROC-mean, DFROC-median, βFROC-10%, βFROC-mean, and βFROC-median were significantly lower in the ER-positive group compared with the ER-negative group. The tumors with PR-negative status showed significantly higher βFROC-10%, βFROC-mean, and βFROC-median values than those with PR-positive status. The values of DFROC-skewness, βFROC-10%, and βFROC-mean exhibited significant differences in differentiating the triple-negative subtype and the Luminal subtype. Conclusions: FROC-based histogram analysis yields diagnostic performance comparable to ADC for benign vs. malignant classification, while providing richer associations with ER/PR status, proliferation, and nodal involvement, reflecting microstructural heterogeneity not captured by mono-exponential diffusion.