Preoperative prediction of histological types and Ki-67 expression in rectal tumors using time-dependent diffusion MRI quantitative parameters

Abstract Objective To investigate the clinical value of Time-dependent diffusion MRI (t d -dMRI) quantitative parameters in differentiating between rectal neuroendocrine neoplasms (rNEN) and rectal adenocarcinoma (RAC) and in distinguishing between high and low Ki-67 expression in RAC. Materials and methods This two-center study prospectively enrolled 90 patients with rectal tumors who underwent t d -dMRI (January 2024 to March 2025), including 85 with RAC (development cohort, n = 61, validation cohort, n = 24) and 5 with rNEN. The t d -dMRI-derived microstructural parameters were estimated using the IMPULSED model, and apparent diffusion coefficient (ADC) values were measured at different diffusion times. Firth’s logistic regression was performed for rNEN-associated parameter selection, while standard logistic regression identified independent predictors to build a combined model for high Ki-67 stratification. Receiver operating characteristic analysis evaluated diagnostic performance. The t d -dMRI-derived microstructural parameters were validated against histopathologic measurements. Results Significant differences in several t d -dMRI parameters were observed between rNENs and RACs, as well as between groups with high and low Ki-67 expression. In the small rNEN cohort, t d -dMRI parameters showed promising preliminary diagnostic performance in distinguishing rNEN from RAC (AUC = 0.882). For predicting high Ki-67 expression in RAC, the combined model yielded an AUC of 0.916 in the development cohort and 0.844 in the validation cohort. Microstructural parameters showed strong correlations with histopathology: cell diameter, cellularity, and intracellular volume fraction reached Pearson correlation coefficients ( r ) of 0.808, 0.773, and 0.765, respectively (all p < 0.001). Conclusion Quantitative t d -dMRI parameters may serve as a noninvasive tool for evaluating rectal tumor histological types (rNEN vs. RAC) and predicting Ki-67 expression in RAC.