Histogram analysis of IVIM MRI for differentiating glioma IDH-1 status, grade, and Ki-67 expression - a comparison of biexponential models at multiple B-value

Sep 30, 2026

Uploaded by hdkawk

Intravoxel Incoherent Motion (IVIM) diffusion-perfusion MRI is used to quantify tumor microstructural heterogeneity, yet diffusion/perfusion histogram features have not been systematically linked to glioma IDH-1 status, tumor grade, and Ki-67 expression. This retrospective study analyzes IVIM data from 73 glioma patients and extracts histogram features across multiple B-value ranges, then compares feature separability using multivariate logistic regression, ROC analysis, and decision curve evaluation.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages16

Reading Time40 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Keywords

Background

Methods (continued)

Introduction (genotyping, IDH, Ki-67)

IVIM and B-value selection

Su etal. BMC Medical Imaging (2026) 26:10 [https://doi.org/10.1186/s12880-025-02101-5](https://doi.org/10.1186/s12880-025-02101-5) BMC Medical Imaging RESEARCH Open Access Histogram analysis of IVIM MRI for differentiating glioma IDH-1 status, grade, and Ki-67 expression: a comparison of biexponential models at multiple B-value Yifei Su 1†, Lei Ji2†, Junhao Wang3, Ding Jia2, Xuanchen Liu 1, Xiaochen Niu4, Yaoju Meng 1, Chunhong Wang2*, Yexin He5* and Hongming Ji 1* Abstract Background Intravoxel Incoherent Motion (IVIM) offers valuable insights into tumor microstructural heterogeneity through diffusion and perfusion. However, the relationship between these features and glioma grading, IDH-1 status, and Ki-67 expression remains insufficiently explored, and there is no consensus on the optimal B-value range for IVIM imaging, which may impact the consistency of results. Methods We retrospectively analyzed IVIM data from 73 glioma patients (mean age, 52.83 years ± 12.79 [SD]; 40 male) . Histogram features of IVIM parameters, calculated with four B-value ranges (0-800, 0-1200, 0-1500, and 0–2000 s/mm²), were extracted from the tumor parenchyma. These features were compared across different IDH-1 status, tumor grade, and Ki-67 proliferation index. After checking for multicol linearity, parameters with significant differences were further analyzed using logistic regression, receiver operating characteristic analysis, and decision curve analysis to compare their performance. And Bonferroni correction was applied to adjust p-values for multiple comparisons. Results Independent risk factors of IDH-1 status, tumor grade and Ki-67 level were identified through multivariate logistic regression in B800, B1200, B1500 and B2000 model. ROC analysis revealed that all models showed similar AUCs for IDH-1 status, tumor grade, and Ki-67 level prediction. The B800 model had the highest specificity (0 . 87) for IDH-1 status prediction, the B1500 model had the highest specificity (0 . 91) for tumor grade prediction, and the B1200 model †Yifei Su and Lei Ji contributed to the work equally and should be regarded as co-first authors. *Correspondence: Chunhong Wang [wang15934150741@163.com](wang15934150741@163.com)[ ](wang15934150741@163.com)Yexin He[heyexinty2000@sina.com](heyexinty2000@sina.com)[ ](heyexinty2000@sina.com)Hongming Ji [hongmingj@sina.com](hongmingj@sina.com) Full list of author information is available at the end of the article © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Su et al. BMC Medical Imaging (2026) 26:10 Page 2 of 16 exhibited the highest sensitivity (0 . 94) for Ki-67 level prediction. The B1500 and B 2000 model demonstrates the best net benefit in most threshold ranges for IDH-1 status, tumor grade and Ki-67 level identification. Conclusion Histogram features of IVIM parameters demonstrated well performance in distinguishing glioma characteristics and the maximum 1500 s/mm2 ma

FAQs

What imaging method does the study evaluate for glioma characterization?

The study evaluates IVIM MRI, using diffusion-weighted signals to derive perfusion and diffusion information without contrast.

How are histogram features extracted and compared?

Histogram features of IVIM parameters are extracted from tumor parenchyma using four B-value ranges, then compared across IDH-1 status, tumor grade, and Ki-67 levels.

Which B-value range shows the best performance for Ki-67 prediction?

The B1200 model shows the highest sensitivity for Ki-67 level prediction, while B1500 and B2000 provide the best net benefit across most threshold ranges.

Sep 30, 2026

Uploaded by hdkawk