EFFECTIVENESS OF DIFFUSION-WEIGHTED MRI IN DETECTING PROSTATE CANCER COMPARED TO HISTOPATHOLOGY AS THE GOLD STANDARD
Main Article Content
Keywords
Prostate Cancer; Diffusion-Weighted MRI; Histopathology; Diagnostic Accuracy; Apparent Diffusion Coefficient; Gleason Score; Multiparametric MRI; Prostate Biopsy
Abstract
Background: Prostate cancer is a cancer that is very popular among men in the world. Early and correct diagnosis is a key to the timely treatment and better results. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been utilized as a very useful non-invasive diagnostic technique to detect malignant lesions in the prostate gland, with histopathology being the ultimate diagnostic method.
Objective: The purpose of the study is to compare the effectiveness and diagnostic accuracy of diffusion-weighted MRI and histopathology as the gold standard in the diagnosis of prostate cancer.
Methodology: It is a cross-sectional, diagnostic accuracy study, which was carried out at University Medical and Dental College, Faisalabad, Pakistan, in the period between September, 2024 and February, 2025. One hundred and twenty male patients who had an elevated PSA test or had clinical indicators of prostate cancer underwent diffusion-weighted magnetic resonance imaging and then underwent prostate biopsy or prostatectomy. Radiologists with high level of experience interpreted MRI and compared it with histopathology findings. The calculation of sensitivity, specificity, predictive values, and the overall diagnostic accuracy was done.
Result: The histopathology tests proved that 70 of them had prostate cancer with 50 cases being benign. Diffusion-weighted MRI identified 64 cases of true positives and 40 cases of true negative. Sensitivity of DW-MRI was 91.4, specificity was 80.0, positive predictive value was 86.5, negative predictive value was 87.0 and the overall diagnostic accuracy was 86.7. The restriction of diffusion was more often closely related to high Gleason grade tumors, which suggests prognostic value.
Conclusion: Diffusion-weighted MRI has high diagnostic accuracy in the diagnosis of prostate cancer and it has a good agreement with histopathology. Its non-invasive characteristics and the potential to determine the level of tumor aggressiveness facilitate the inclusion of the method into the everyday clinical activity of diagnosing and managing the early stages of prostate cancer.
References
2. Hameed N, Hussain M, Ahmad N, Ibrahim A, Ali S, Rehman R. Diagnostic accuracy of diffusion weighted magnetic resonance imaging in diagnosing muscle invasion in urinary bladder cancer taking histopathology as gold standard. Annals of Punjab Medical College. 2021;15(3):173–180.
3. Shams RA, Gilani SA, Maryam S, Babar A, Farooq SY. Diagnostic accuracy of diffusion weighted magnetic resonance imaging for prostatic carcinoma taking histopathology as gold standard. Ophthalmology Update. 2021;19(4):23–27.
4. Ying L, Hu K, Qiu H, Ying L. A study on diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging combined with diffusion-weighted imaging in patients with prostate cancer. Journal of Men’s Health. 2025;21(7):69–78.
5. McGarry SD, Brehler M, Bukowy JD, Lowman AK, Bobholz SA, Duenweg SR, Banerjee A, Hurrell SL, Malyarenko D, Chenevert TL, Cao Y. Multi-site concordance of diffusion-weighted imaging quantification for assessing prostate cancer aggressiveness. Journal of Magnetic Resonance Imaging. 2022;55(6):1745–1758.
6. Sushentsev N, Caglic I, Rundo L, Kozlov V, Sala E, Gnanapragasam VJ, Barrett T. Serial changes in tumour measurements and apparent diffusion coefficients in prostate cancer patients on active surveillance with and without histopathological progression. British Journal of Radiology. 2022;95(1131):20210842.
7. Samad M, Nisar S, Abbas S, Malik M, Shahzad K, Malik MH. Diagnostic accuracy of diffusion weighted magnetic resonance imaging for the detection of prostate cancer, keeping histopathology as the gold standard. Pakistan Journal of Medical & Health Sciences. 2024;18(8):15.
8. Chatterjee A, Antic T, Gallan AJ, Paner GP, Lin LI, Karczmar GS, Oto A. Histological validation of prostate tissue composition measurement using hybrid multi-dimensional MRI: agreement with pathologists’ measures. Abdominal Radiology. 2022;47(2):801–813.
9. Chatterjee A, Dwivedi DK. MRI-based virtual pathology of the prostate. Magnetic Resonance Materials in Physics, Biology, and Medicine. 2024;37(4):709–720.
10. Yan X, Ma K, Zhu L, Pan Y, Wang Y, Shi J, Mai X. The value of apparent diffusion coefficient values in predicting Gleason grading of low to intermediate-risk prostate cancer. Insights into Imaging. 2024;15(1):137.
11. Mehmood A, Hafeez MA, Ansari F. Comparative study of magnetic resonance imaging and trans-rectal ultrasound in identifying clinically significant prostate cancer among diverse ethnic groups using histopathology as the gold standard.
12. Patsanis A, Sunoqrot MR, Langørgen S, Wang H, Selnæs KM, Bertilsson H, Bathen TF, Elschot M. A comparison of generative adversarial networks for automated prostate cancer detection on T2-weighted MRI. Informatics in Medicine Unlocked. 2023;39:101234.
13. Li D, Han X, Gao J, Zhang Q, Yang H, Liao S, Guo H, Zhang B. Deep learning in prostate cancer diagnosis using multiparametric magnetic resonance imaging with whole-mount histopathology referenced delineations. Frontiers in Medicine. 2022;8:810995.
14. Lou X. Analysis of the diagnostic efficacy and clinical utility of multiparametric MRI in prostate cancer diagnosis. Cancer Management and Research. 2025;1803–1812.
15. Michaely HJ, Aringhieri G, Cioni D, Neri E. Current value of biparametric prostate MRI with machine-learning or deep-learning in the detection, grading, and characterization of prostate cancer: a systematic review. Diagnostics. 2022;12(4):799.
16. Liu Z, Gu WJ, Wan FN, Chen ZZ, Kong YY, Liu XH, Ye DW, Dai B. Deep learning network enhances imaging quality of low-b-value diffusion–weighted imaging and improves lesion detection in prostate cancer. BMC Cancer. 2025;25(1):953.
17. Sheng Y, Chang H, Xue K, Chen J, Jiao T, Cui D, Wang H, Zhang G, Yang Y, Zeng Q. Characterization of prostatic cancer lesion and Gleason grade using a continuous-time random-walk diffusion model at high b-values. Frontiers in Oncology. 2024;14:1389250.
18. Zhu G, Luo J, Ouyang Z, Cheng Z, Deng Y, Guan Y, Du G, Zhao F. The assessment of prostate cancer aggressiveness using a combination of quantitative diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Cancer Management and Research. 2021;5287–5295.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Dr Muhammad Zafar Iqba
Assistant Professor, University Medical and Dental College, Faisalabad, Pakistan
Email: [email protected]
Dr Muhammad Shahid Saleem
MBBS, MCPS, FCPS, Assistant Professor, FMU, Pakistan
Email: [email protected]
Dr Shehzad Aslam
Senior Registrar, Chaudhry Muhammad Akram Teaching and Research Hospital, Azra Naheed Medical College, Superior University, Lahore, Pakistan
Email: [email protected]
