Diagnostic Accuracy of Diffusion-Weighted Imaging in Multiparametric MRI in Detecting Carcinoma Prostate Taking Histopathology as Gold Standard
DOI:
https://doi.org/10.70749/ijbr.v3i7.3093Keywords:
Prostatic Neoplasms, Magnetic Resonance Imaging, Diffusion-Weighted Imaging, Prostate-Specific Antigen, Gleason Score.Abstract
Background: Carcinoma prostate is a type of malignancy that is prevalent in geriatric males and is a leading cause of morbidity across the globe. Timely and proper diagnosis is the key to successful management. Multiparametric magnetic resonance imaging (mpMRI), especially diffusion-weighted imaging (DWI) has become an effective non-invasive diagnostic modality use to identify clinically relevant prostate cancer. Objective: To determine the diagnostic accuracy of diffusion-weighted imaging as part of multiparametric MRI in detecting carcinoma prostate, taking histopathology as the gold standard. Methods: This prospective study of diagnostic accuracy is a diagnostic accuracy study that was carried out at the Radiology Department, Mardan Medical Complex (MMC), MTI Mardan, during the period of November 2024 to May 2025. They included 150 male patients who were clinically suspected to have carcinoma prostate, but found in consecutive non-probability sampling. Every patient had a multiparametric MRI with diffusion-weighted imaging and apparent diffusion coefficient (ADC) mapping. PI-RADS version 2 was used to classify lesions, with a score of 3 or higher being positive. A gold standard was the histopathological examination. Data were broken with the help of SPSS version 25 and the parameters of diagnostic accuracy were calculated. Results: The mean age of patients was 65.2 ± 9.1 years, with the majority above 60 years. Histopathology confirmed carcinoma in 58.7% of patients. mpMRI demonstrated a sensitivity of 90.9%, specificity of 74.2%, positive predictive value of 83.3%, and negative predictive value of 85.2%, with an overall diagnostic accuracy of 84.0%. Lower ADC values were significantly associated with malignancy, and a strong negative correlation was observed between ADC values and Gleason score (r = -0.64, p < 0.001). Conclusion: Multiparametric MRI with diffusion-weighted imaging shows high sensitivity and good diagnostic accuracy in detecting carcinoma prostate. It serves as a reliable non-invasive tool for diagnosis, particularly for ruling out disease and guiding biopsy decisions
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References
1. Dwivedi, D. K., & Jagannathan, N. R. (2022). Emerging MR methods for improved diagnosis of prostate cancer by multiparametric MRI. Magnetic Resonance Materials in Physics, Biology and Medicine, 35(4), 587-608.
https://doi.org/10.1007/s10334-022-01031-5
2. Wichtmann, B.D., et al. Multiparametric MRI in the diagnosis of prostate cancer: physical foundations, limitations, and prospective advances of diffusion-weighted MRI. in RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. 2021. Georg Thieme Verlag KG.
3. Tamada, T., Ueda, Y., Ueno, Y., Kojima, Y., Kido, A., & Yamamoto, A. (2021). Diffusion-weighted imaging in prostate cancer. Magnetic Resonance Materials in Physics, Biology and Medicine, 35(4), 533-547.
https://doi.org/10.1007/s10334-021-00957-6
4. Li, D., Han, X., Gao, J., Zhang, Q., Yang, H., Liao, S., Guo, H., & Zhang, B. (2022). Deep learning in prostate cancer diagnosis using Multiparametric magnetic resonance imaging with Whole-Mount histopathology referenced delineations. Frontiers in Medicine, 8.
https://doi.org/10.3389/fmed.2021.810995
5. Gibbons, M., Starobinets, O., Simko, J. P., Kurhanewicz, J., Carroll, P. R., & Noworolski, S. M. (2022). Identification of prostate cancer using multiparametric MR imaging characteristics of prostate tissues referenced to whole Mount histopathology. Magnetic Resonance Imaging, 85, 251-261.
https://doi.org/10.1016/j.mri.2021.10.008
6. Ranasinghe, W. K., Troncoso, P., Surasi, D. S., Ibarra Rovira, J. J., Bhosale, P., Szklaruk, J., Kokorovic, A., Wang, X., Elsheshtawi, M., Zhang, M., Aparicio, A., Chapin, B. F., & Bathala, T. K. (2022). Defining diagnostic criteria for prostatic ductal adenocarcinoma at Multiparametric MRI. Radiology, 303(1), 110-118.
https://doi.org/10.1148/radiol.204732
7. Sun, Z., Wu, P., Cui, Y., Liu, X., Wang, K., Gao, G., ... & Wang, X. (2023). Deep‐learning models for detection and localization of visible clinically significant prostate cancer on multi‐parametric MRI. Journal of Magnetic Resonance Imaging, 58(4), 1067-1081.
https://doi.org/10.1002/jmri.28608
8. Raczeck, P., Frenzel, F., Woerner, T., Graeber, S., Bohle, R. M., Ziegler, G., Buecker, A., & Schneider, G. K. (2021). Noninferiority of Monoparametric MRI versus Multiparametric MRI for the detection of prostate cancer. Investigative Radiology, 57(4), 233-241.
https://doi.org/10.1097/rli.0000000000000830
9. Ingole, S. M., Mehta, R. U., Kazi, Z. N., & Bhuyar, R. V. (2021). Multiparametric magnetic resonance imaging in evaluation of clinically significant prostate cancer. Indian Journal of Radiology and Imaging.
https://doi.org/10.1055/s-0041-1730093
10. De Perrot, T., Sadjo Zoua, C., Glessgen, C. G., Botsikas, D., Berchtold, L., Salomir, R., De Seigneux, S., Thoeny, H. C., & Vallée, J. (2022). Diffusion-weighted MRI in the Genitourinary system. Journal of Clinical Medicine, 11(7), 1921.
https://doi.org/10.3390/jcm11071921
11. Singla, A., Deep, N., Naik, S., Mohakud, S., Nayak, P., & Sable, M. (2023). Correlation of multiparametric MRI with histopathological grade of peripheral zone prostate carcinoma. Journal of Cancer Research and Therapeutics, 19(Suppl 2), S569-S576.
https://doi.org/10.4103/jcrt.jcrt_280_22
12. Yoshida, S., Takahara, T., Arita, Y., Sakaino, S., Katahira, K., & Fujii, Y. (2021). Whole‐body diffusion‐weighted magnetic resonance imaging: Diagnosis and follow up of prostate cancer and beyond. International Journal of Urology, 28(5), 502-513.
https://doi.org/10.1111/iju.14497
13. Gaudiano, C., Renzetti, B., De Fino, C., Corcioni, B., Ciccarese, F., Bianchi, L., Schiavina, R., Droghetti, M., Giunchi, F., Brunocilla, E., & Fiorentino, M. (2023). Multiparametric magnetic resonance imaging for the differential diagnosis between granulomatous prostatitis and prostate cancer: A literature review to an intriguing diagnostic challenge. Frontiers in Oncology, 13.
https://doi.org/10.3389/fonc.2023.1178430
14. Zhang, Z., Wu, H. H., Priester, A., Magyar, C., Afshari Mirak, S., Shakeri, S., Mohammadian Bajgiran, A., Hosseiny, M., Azadikhah, A., Sung, K., Reiter, R. E., Sisk, A. E., Raman, S., & Enzmann, D. R. (2020). Prostate microstructure in prostate cancer using 3-T MRI with diffusion-relaxation correlation spectrum imaging: Validation with Whole-Mount digital histopathology. Radiology, 296(2), 348-355.
https://doi.org/10.1148/radiol.2020192330
15. Xing, P., Chen, L., Yang, Q., Song, T., Ma, C., Grimm, R., Fu, C., Wang, T., Peng, W., & Lu, J. (2021). Differentiating prostate cancer from benign prostatic hyperplasia using whole-lesion histogram and texture analysis of diffusion- and T2-weighted imaging. Cancer Imaging, 21(1).
https://doi.org/10.1186/s40644-021-00423-5
16. Midiri, M. Role of the Apparent Diffusion Coefficient (ADC) values analysis in Diffusion Weighted Imaging (DWI-MR) in the characterization of prostatic disease on Multiparametric Magnetic Resonance Imaging (Mp-MRI) of the prostate.
https://iris.unipa.it/retrieve/handle/10447/265253/510380/PhD%20Purpura%20Tesi.pdf
17. Zhu, G., Luo, J., Ouyang, Z., Cheng, Z., Deng, Y., Guan, Y., Du, G., & Zhao, F. (2021). 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, 13, 5287-5295.
https://doi.org/10.2147/cmar.s319306
18. Bashkanov, O., Rak, M., Meyer, A., Engelage, L., Lumiani, A., Muschter, R., & Hansen, C. (2023). Automatic detection of prostate cancer grades and chronic prostatitis in biparametric MRI. Computer Methods and Programs in Biomedicine, 239, 107624.
https://doi.org/10.1016/j.cmpb.2023.107624
19. McGarry, S. D., Brehler, M., Bukowy, J. D., Lowman, A. K., Bobholz, S. A., Duenweg, S. R., ... & LaViolette, P. S. (2022). Multi‐Site Concordance of Diffusion‐Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness. Journal of Magnetic Resonance Imaging, 55(6), 1745-1758.
https://doi.org/10.1002/jmri.27983
20. Gibbons, M., Simko, J. P., Carroll, P. R., & Noworolski, S. M. (2023). Prostate cancer lesion detection, volume quantification and high-grade cancer differentiation using cancer risk maps derived from multiparametric MRI with histopathology as the reference standard. Magnetic Resonance Imaging, 99, 48-57.
https://doi.org/10.1016/j.mri.2023.01.006
21. Daimiel Naranjo, I., Lo Gullo, R., Saccarelli, C., Thakur, S. B., Bitencourt, A., Morris, E. A., Jochelson, M. S., Sevilimedu, V., Martinez, D. F., & Pinker-Domenig, K. (2020). Diagnostic value of diffusion-weighted imaging with synthetic B-values in breast tumors: Comparison with dynamic contrast-enhanced and multiparametric MRI. European Radiology, 31(1), 356-367.
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