Related Experiment Video
Updated: Jan 12, 2026
A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
Published on: March 21, 2025
Improving the diagnostic efficacy of MR cytometry in prostate cancer imaging
Jiahui Zhang1, Fan Liu2, Qianyu Peng1,3
1Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
Objectives:
To evaluate the improved diagnostic efficacy of the emerging non-invasive microstructure imaging technique, MR cytometry, in differentiating benign and malignant prostate lesions, after removing perfusion effect and introducing histogram features.
Methods:
This study included 33 patients with prostate cancer and 39 patients with benign lesions, who underwent time-dependent diffusion MRI examinations, including pulsed gradient spin-echo (PGSE) diffusion-weighted imaging (DWI) and oscillating gradient spin-echo (OGSE) DWI at frequencies of 20 Hz and 40 Hz. A mono-exponential signal-fitting method was used to eliminate perfusion effects. Apparent diffusion coefficients (ADC), MR-cytometry-derived microstructural parameters (cell diameter [Formula: see text], intracellular volume fraction [Formula: see text], extracellular diffusivity [Formula: see text] and cellularity [Formula: see text]) and corresponding histogram features were calculated to differentiate lesions. Mann-Whitney U-tests were used to evaluate the differences between benign and malignant lesions. Uni- and multi-variate logistic regressions were performed and calculated the area under the receiver operating characteristic (AUC) to quantify the diagnostic performance of classifiers.
Results:
Removal of perfusion effect improved the significance level of the difference in the cell diameter [Formula: see text] (from [Formula: see text] to [Formula: see text]), where [Formula: see text] is larger in the benign group. The diagnostic efficacy of a single histogram feature was comparable to that of the corresponding ADC or microstructural parameter mean value. Both removing perfusion effect and incorporating histogram features can improve the AUC of the combined regression models (from 0.885 to 0.918 and 0.908, respectively). Combining both strategies further enhanced the AUC to 0.934.
Conclusion:
In MR cytometry analysis, removing the perfusion effect and incorporating histogram features of ADCs and microstructural parameters can provide better diagnostic efficacy.
