A study on the histogram of DCE-MRI pharmacokinetic parameters for predicting endocrine therapy response in prostate cancer

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• Clinical Article •

LI Jing ZOU Caixia PAN Nini CHEN Jun HUANG Gang

Cite this article as: LI J, ZOU C X, PAN N N, et al. A study on the histogram of DCE-MRI pharmacokinetic parameters for predicting endocrine therapy response in prostate cancer[J]. Chin J Magn Reson Imaging, 2025, 16(4): 70-80. DOI:10.12015/issn.1674-8034.2025.04.011.

Abstract

[Abstract] Objective To explore the value of predicting the response of prostate cancer (PCa) to endocrine therapy based on the histogram features of pharmacokinetic parameters of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).Materials and Methods Retrospectively collect the clinical and imaging data of PCa patients from Zhangye People's Hospital Affiliated to Hexi University from January 2018 to October 2023 and Gansu Provincial People's Hospital from February 2020 to February 2023, two weeks before endocrine therapy. A total of 105 cases were collected from Zhangye People's Hospital Affiliated to Hexi University, which were divided into a training set (73 cases) and an internal validation set (32 cases) at a ratio of 7∶3. A total of 47 cases were collected from Gansu Provincial People's Hospital as an external validation set. Select the original DCE-MRI images, and obtain the pseudo-color maps of pharmacokinetic parameters including volume transfer constant (K^trans), rate constant (Kep), and extravascular extracellular volume fraction (Ve) through the Siemens Syngovia workstation. In the 3D Slicer software, referring to the axial T2-weighted imaging (T2WI), delineate the region of interest (ROI) of the whole prostate gland layer by layer on the pseudo-color maps of pharmacokinetic parameters, and then extract the histogram features. Through dimensionality reduction by the least absolute shrinkage and selection operator (LASSO), 8 optimal features were screened out and the histogram features was calculated. Univariate and backward multivariate logistic regression were used to analyze the independent predictive factors of the good-response group and the poor-response group of endocrine therapy, and a clinical model, a histogram features model, and a combined model were constructed. The area under the curve (AUC) was calculated using the receiver operating characteristic (ROC) curve, and the calibration curve and decision curve were used to evaluate the performance of the model. The efficacy of each model was evaluated by the DeLong test. Finally, a nomogram was drawn based on the independent predictive factors of the combined model.Results In the training set, internal validation set, and external validation set, there were statistically significant differences in Gleason score, MRI-T stage, and histogram features between the good-response group and the poor-response group (P < 0.001). Backward multivariate logistic regression analysis showed that the Gleason score (OR = 0.925, 95% CI: 0.859 to 0.958, P = 0.038), MRI-T stage (OR = 0.871, 95% CI: 0.800 to 0.949, P = 0.002), and histogram features (OR = 0.096, 95% CI: 0.056 to 0.137, P < 0.001) were independent predictive factors for the response of PCa to endocrine therapy. The AUCs of the clinical model in the training set, internal validation set, and external validation set were 0.857 (95% CI: 0.774 to 0.939), 0.953 (95% CI: 0.888 to 0.996), and 0.808 (95% CI: 0.676 to 0.941), respectively. The AUCs of the histogram features model in the training set, internal validation set, and external validation set were 0.874 (95% CI: 0.769 to 0.951), 0.816 (95% CI: 0.664 to 0.967), and 0.674 (95% CI: 0.517 to 0.831), respectively. The AUCs of the combined model in the training set, internal validation set, and external validation set were 0.951 (95% CI: 0.906 to 0.994), 0.973 (95% CI: 0.922 to 0.995), and 0.830 (95% CI: 0.699 to 0.960), respectively. The analysis of the decision curve and calibration curve showed that the combined model had good clinical application value and stability. The DeLong test and NRI value showed that the predictive efficacy of the combined model was better than that of the clinical model and the histogram features model.Conclusions The histogram features of DCE-MRI pharmacokinetic parameters is an independent predictive factor for predicting the response of PCa to endocrine therapy. The combined model has good value in predicting the response of PCa to endocrine therapy, providing new ideas for clinical treatment decisions.

[Keywords] prostate cancer；magnetic resonance imaging；dynamic contrast enhancement；histogram；endocrine therapy

Contributor Information

LI Jing^{1,
2} ZOU Caixia¹ PAN Nini³ CHEN Jun⁴ HUANG Gang^3*

¹ The First Clinical Medical College of Gansu University of Traditional Chinese Medicine, Lanzhou 730000, China

² Institute of Imaging, Zhangye People's Hospital, Hexi University, Zhangye 734000, China

³ Department of Radiology, Gansu Provincial People's Hospital, Lanzhou 730000, China

⁴ Department of Health Imaging Diagnosis, Bayer Healthcare Co., Ltd, Wuhan 430000, China

Corresponding author: HUANG G, E-mail: keen0999@163.com

Conflicts of interest None.

Publication Information

Received 2024-10-22

Accepted 2025-04-10

DOI: 10.12015/issn.1674-8034.2025.04.011

Text

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