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

结合临床信息的多参数磁共振扩散模型预测胰腺导管腺癌中Ki-67表达:一项前瞻性队列研究

阅读:1

Abstract

PURPOSE: 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). METHODS: 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. RESULTS: 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). CONCLUSION: 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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。