Analisis Informasi Diagnostik antara Small dan Large Field of View Diffusion Weighted Imaging pada Pemeriksaan MRI Pelvis Kasus Kanker Prostat
DOI:
https://doi.org/10.31983/jimed.v12i2.13562Keywords:
MRI Pelvis, Prostate Cancer, Small FOV DWI, Large FOV DWIAbstract
Diffusion-Weighted Imaging (DWI) allows characterization of tumors by measurement of cell density which is usually increased in the tumor area. In pelvic MRI examinations, conventional DWI is a routine protocol used but has limitations in the form of susceptibility artifact and long scan times, making it less optimal for detecting small lesions. Small FOV DWI is able to minimize susceptibility artifact with a shorter scan time, but the superiority in displaying diagnostic information between the two sequences is not yet known. Therefore, this study aims to determine the difference in diagnostic information and the most optimal sequence between small FOV DWI and conventional DWI on pelvic MRI of prostate cancer cases.
This research employs a quantitative, experimental approach with a sample of 5 patients diagnosed with prostate cancer. The study was conducted at RSUP dr. Sardjito Yogyakarta. The resulting images underwent diagnostic information assessment were then processed using Cohen’s kappa test and Wilcoxon test to determine the differences in diagnostic information and to identify the most optimal sequence based on the mean rank results.
The results of the Wilcoxon test indicated no significant difference between the small FOV DWI and conventional DWI sequences in assessing diagnostic information, with a p-value of 0.068. conventional DWI images yielded the highest diagnostic information, as indicated by a mean rank of 2.50, suggesting that this sequence is optimal for visualizing tumor extension in prostate cancer. Future research should expand the sample size and incorporate image quality assessments.
References
Brendle, C. et al. (2016) ‘Diffusion-weighted imaging in the assessment of prostate cancer: Comparison of zoomed imaging and conventional technique’, European Journal of Radiology, 85(5), pp. 893–900. Available at: https://doi.org/10.1016/j.ejrad.2016.02.020.
Goljan, E.F.M. (2014) RAPID REVIEW PATHOLOGY. 4th edn. Elsevier.
Hansen, J.T., Netter, F.H. and Machado, C.A.G. (2019) Netter’s Clinical Anatomy. 4th edn. Elsevier, Inc.
Hu, L. et al. (2021) ‘Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study’, European Radiology, 31(3), pp. 1760–1769. Available at: https://doi.org/10.1007/S00330-020-07227-4/METRICS.
Kirnantoro, D.H. (2017) Anatomi Fisiologi. Pustaka Baru Press.
Kumar, V., Abbas, K.A. and Aster, C.J. (2018) Robbins Basic Pathology. 10th edn. Philadelphia, Pennsylvania: Elsevier.
Maurer, M.H. and Heverhagen, J.T. (2017) ‘Diffusion weighted imaging of the prostate—principles, application, and advances’, Translational Andrology and Urology, 6(3), p. 490. Available at: https://doi.org/10.21037/TAU.2017.05.06.
Westbrook, C. (2014) Handbook of MRI technique. 4th edn. Southern Gate, Chichester: Wiley Blackwell.
Zamboglou, C. and Kirste, S. (2024) ‘Prostate Cancer’, Target Volume Definition in Radiation Oncology, pp. 299–318. Available at: https://doi.org/10.1007/978-3-031-45489-9_14.
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