What type of study is this?

This is a Prospective cohort study study.

September 10, 2025Open Access

Multi-parametric MRI diffusion models combined with clinical information for predicting Ki-67 expression in pancreatic ductal adenocarcinoma: a prospective cohort study

Key Points

The combined prediction model achieved an area under the curve (AUC) of 0.913 in assessing Ki-67 expression in pancreatic ductal adenocarcinoma.
Patients with high Ki-67 expression had significantly lower monocyte counts and higher IVIM f-values compared to those with low expression.
Individual predictive models for monocyte count, IVIM f-value, and DKI-MD demonstrated AUCs ranging from 0.732 to 0.800, reflecting decent diagnostic accuracy.
This approach offers a potentially reliable, non-invasive diagnostic tool, which supports precision medicine strategies in managing pancreatic cancer.

Abstract

To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters combined with clinical information for predicting Ki-67 expression in pancreatic ductal adenocarcinoma (PDAC). This prospective cohort study enrolled 65 patients with histopathologically confirmed PDAC between January 2024 and May 2025. All patients underwent 3.0T MRI including conventional sequences and advanced diffusion-weighted imaging sequences. Clinical data and laboratory parameters were collected within one week before surgery or biopsy. Ki-67 expression was assessed using immunohistochemical staining with 50% as the cutoff value. Two radiologists independently performed quantitative measurements with excellent inter-observer reliability (ICC > 0.85). Univariate and multivariate logistic regression analyses identified independent predictors. ROC curve analysis and DeLong test evaluated diagnostic performance. Based on Ki-67 expression threshold of 50%, 48 patients (73.8%) were classified as low expression and 17 patients (26.2%) as high expression. Compared to the low Ki-67 group, the high expression group demonstrated significantly lower monocyte count (0.35 ± 0.09 vs. 0.49 ± 0.16 × 10⁹/L, P = 0.001), higher IVIM f-value (14.08 ± 3.41% vs. 10.90 ± 3.83%, P = 0.004), and lower DKI-MD (1.26 ± 0.17 vs. 1.65 ± 0.17 × 10⁻³ mm²/s, P < 0.001). Individual prediction models achieved AUCs of 0.763 (monocyte count), 0.732 (IVIM f-value), and 0.800 (DKI-MD). The combined prediction model integrating these three parameters demonstrated excellent diagnostic performance with AUC of 0.913 (95% CI: 0.841-0.985), sensitivity of 82.4%, and specificity of 83.3%, significantly outperforming all individual models (P < 0.001). This multi-parametric combined prediction model achieves excellent diagnostic performance for preoperative non-invasive assessment of Ki-67 expression status in PDAC, providing a reliable tool for precision medicine practice and personalized treatment strategies.

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