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Diagnostics
Volume 15
Issue 21
10.3390/diagnostics15212722
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Article

Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio

by
Jiangqin Ma
1,†,
Xiling Gu
2,†,
Zhonglin Zhang
1,
Jun Chen
3,
Yunfan Liu
1,
Yang Qiu
1,
Guangyong Ai
1,
Xuxiang Jia
4,
Zhenghao Li
4,
Bo Xiang
5,* and
Xiaojing He
1,*
1
Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
2
Department of Pathology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
3
Beijing Institute of Technology, Beijing 100081, China
4
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
5
Department of Radiology, Yongchuan Hospital of Chongqing Medical University, Chongqing 402115, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2025, 15(21), 2722; https://doi.org/10.3390/diagnostics15212722 (registering DOI)
Submission received: 18 August 2025 / Revised: 23 September 2025 / Accepted: 24 October 2025 / Published: 27 October 2025
(This article belongs to the Special Issue Innovations in Medical Imaging for Precision Diagnostics)
Versions Notes

Abstract

Objectives: This study aimed to develop and validate a biparametric MRI (bpMRI)-based radiomics model for the noninvasive prediction of tumor–stroma ratio (TSR) in prostate cancer (PCa). Additionally, we sought to explore lesion distribution patterns in the peripheral zone (PZ) and transition zone (TZ) to provide deeper insights into the biological behavior of PCa. Methods: This multicenter retrospective study included 223 pathologically confirmed PCa patients, with 146 for training and 39 for internal validation at Center 1, and 38 for external testing at Center 2. All patients underwent preoperative bpMRI (T2WI, DWI acquired with a b-value of 1400 s/mm2, and ADC maps）, with TSR histopathologically quantified. Regions of interest (ROIs) were manually segmented on bpMRI images using ITK-SNAP software (version 4.0.1), followed by high-throughput radiomic feature extraction. Redundant features were eliminated via Spearman correlation analysis and least absolute shrinkage and selection operator (LASSO) regression. Five machine learning (ML) classifiers—Logistic Regression (LR), Support Vector Machine (SVM), BernoulliNBBayes, Ridge, and Stochastic Gradient Descent (SGD)—were trained and optimized. Model performance was rigorously evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The Ridge demonstrated superior diagnostic performance, achieving AUCs of 0.846, 0.789, and 0.745 in the training, validation, and test cohorts, respectively. Lesion distribution analysis revealed no significant differences between High-TSR and Low-TSR groups (p = 0.867), suggesting that TSR may not be strongly associated with zonal localization. Conclusions: This exploratory study suggests that a bpMRI-based radiomic model holds promise for noninvasive TSR estimation in prostate cancer and may provide complementary insights into tumor aggressiveness beyond conventional pathology.
Keywords: prostate cancer; tumor–stroma ratio; radiomics; biparametric MRI; machine learning prostate cancer; tumor–stroma ratio; radiomics; biparametric MRI; machine learning

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MDPI and ACS Style

Ma, J.; Gu, X.; Zhang, Z.; Chen, J.; Liu, Y.; Qiu, Y.; Ai, G.; Jia, X.; Li, Z.; Xiang, B.; et al. Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio. Diagnostics 2025, 15, 2722. https://doi.org/10.3390/diagnostics15212722

AMA Style

Ma J, Gu X, Zhang Z, Chen J, Liu Y, Qiu Y, Ai G, Jia X, Li Z, Xiang B, et al. Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio. Diagnostics. 2025; 15(21):2722. https://doi.org/10.3390/diagnostics15212722

Chicago/Turabian Style

Ma, Jiangqin, Xiling Gu, Zhonglin Zhang, Jun Chen, Yunfan Liu, Yang Qiu, Guangyong Ai, Xuxiang Jia, Zhenghao Li, Bo Xiang, and et al. 2025. "Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio" Diagnostics 15, no. 21: 2722. https://doi.org/10.3390/diagnostics15212722

APA Style

Ma, J., Gu, X., Zhang, Z., Chen, J., Liu, Y., Qiu, Y., Ai, G., Jia, X., Li, Z., Xiang, B., & He, X. (2025). Advancing Prostate Cancer Assessment: A Biparametric MRI (T2WI and DWI/ADC)-Based Radiomic Approach to Predict Tumor–Stroma Ratio. Diagnostics, 15(21), 2722. https://doi.org/10.3390/diagnostics15212722

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