The predictive value of MR cytometry in histological differentiation of rectal cancer: an exploratory study

Question Microstructural parameters obtained from MR cytometry methods and apparent diffusion coefficients showed significant differences between rectal tumors with different histological differentiation grades. Findings The combined regression models, including both microstructural parameters and apparent diffusion coefficient metrics, provided a significantly higher value than the conventional PGSE-based ADC measurement. Clinical relevance Incorporating transcytolemmal water exchange into biophysical modeling can further improve the clinical performance over the impermeable model.

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Publication Details

Journal
European Radiology
Published
2026-01-30
DOI
https://doi.org/10.1007/s00330-026-12341-w
Primary Topic
MRI in cancer diagnosis
Type
article
Field-Weighted Citation Impact
0.00

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article

The predictive value of MR cytometry in histological differentiation of rectal cancer: an exploratory study

Chaoyang Jin, Thorsten Feiweier, Diwei Shi, Junzhong Xu et al.
European Radiology
MRI in cancer diagnosis
article

The predictive value of MR cytometry in histological differentiation of rectal cancer: an exploratory study

Chaoyang Jin, Thorsten Feiweier, Diwei Shi, Junzhong Xu, Zheng Wang, yueluan jiang, Zhuo Shi, Qing Zhao, Hongmei Zhang, Bingjing Wang, Hua Guo, Hongxia Zhong, Lin Li
article en

Abstract

Question Microstructural parameters obtained from MR cytometry methods and apparent diffusion coefficients showed significant differences between rectal tumors with different histological differentiation grades. Findings The combined regression models, including both microstructural parameters and apparent diffusion coefficient metrics, provided a significantly higher value than the conventional PGSE-based ADC measurement. Clinical relevance Incorporating transcytolemmal water exchange into biophysical modeling can further improve the clinical performance over the impermeable model.

European Radiology
Siemens (Germany) (DE), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beijing Forestry University (CN), National Cancer Center (US), Shanxi Provincial Cancer Hospital (CN), Tsinghua–Berkeley Shenzhen Institute (CN), Cancer Hospital of Chinese Academy of Medical Sciences (CN), Siemens Healthcare (United States) (US), Siemens Healthineers (Germany) (DE), Vanderbilt University Medical Center (US), Tsinghua University (CN)
National Science Foundation, National Natural Science Foundation of China, China Postdoctoral Science Foundation
No poverty
Openalex Percentile: Top 72%
MRI in cancer diagnosis
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