Yüksek Dereceli Primer Beyin Tümörleri ile Metastatik Beyin Tümörlerinin Difüzyon MR Bulgularının Karşılaştırılması
Abstract
Amaç: Glioblastomalar en yüksek dereceli ve en ölümcül primer beyin tümörleridir. Beyin dışı dokulardaki kanserlerin beyne metastazı ile ortaya çıkan beyin kitleleri glioblastomaların ayırıcı tanısında yer almaktadır. Bu çalışmada, primer ve metastatik beyin kitlelerinin difüzyon ağırlıklı görüntüleme sinyal özelliklerinin karşılaştırılması ve ayırıcı tanıda faydalı olabilecek bulguların tanımlanması amaçlandı.
Gereç ve Yöntemler: Çalışmaya patolojik olarak glioblastoma tanısı almış hastalar ile patolojik olarak metastaz tanısı almış veya radyolojik olarak beyin metastazı tanısı almış hastalar dahil edildi. 1,5 Tesla tarayıcı ile elde edilen manyetik rezonans görüntüleme incelemelerindeki difüzyon ağırlıklı görüntüleme sinyal özellikleri geriye dönük olarak analiz edildi. Her iki hasta grubunda lezyonların sinyal özellikleri ile kısa ve uzun çapları ölçüldü ve karşılaştırıldı.
Bulgular: Bu çalışmaya 24 glioblastoma ve 30 beyin metastazı olmak üzere toplam 54 hasta dahil edildi. Glioblastoma grubunda difüzyon ağırlıklı görüntülemenin en yaygın sinyal özelliği 20 (%83,3) heterojen hiper ve hipointens alanlar olarak saptandı. Metastaz grubunda en sık görülen sinyal özelliği 16 (%53,3) hastada periferik hiperintens halka ve santral hipointens sinyal olarak saptandı. Lezyon sayısı ile primer beyin tümörü ve metastazlar arasında anlamlı bir ilişki bulunamadı.
Sonuç: Difüzyon ağırlıklı görüntülemede kantitatif değerlendirme yapılmadan sadece sinyal özellikleri kullanılsa da primer ve metastatik beyin kitlelerinin ayırıcı tanısında yardımcı olabilir. İki gruptaki kitlelerin karşılaştırılabilir sinyal özelliklerine sahip olabileceğini unutmamak önemlidir.
References
- Lapointe S, Perry A, Butowski NA. Primary brain tumors in adults. Lancet. 2018;392(10145):432-46.
- Mourad AF, Mohammad HEG, Sayed MM, Ragae MA. What’s the clinical significance of adding diffusion and perfusion MRI in the differentiation of glioblastoma multiforme and solitary brain metastasis? Egypt J Radiol Nucl Med. 2017;48(3):661-9.
- Yazol M, Öner AY. Magnetic resonance imaging in brain gliomas. Trd Sem. 2016;4(1):20-36. Turkish.
- Xiang C, Chen Q, Zha Y. Specific features of primary central nervous system lymphoma in comparison with glioblastoma on conventional MRI. Iran J Radiol. 2019;16(1):e78868.
- Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-weighted imaging: recent advances and applications. Semin Ultrasound CT MR. 2021;42(5):490-506.
- Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion-weighted imaging in patients with brain tumors. Am J Neuroradiol. 2001;22(6):1081-8.
- Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J. MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol. 1994;162(3):671-7.
- Yan Q, Li F, Cui Y, Wang Y, Wang X, Jia W, et al. Discrimination between glioblastoma and solitary brain metastasis using conventional MRI and diffusion-weighted imaging based on a deep learning algorithm. J Digit imaging. 2023;36(4):1480-8.
- Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, et al. Machine learning for semi-automated classification of glioblastoma, brain metastasis, and central nervous system lymphoma using magnetic resonance advanced imaging. Ann Transl Med. 2019;7(11):232.
- Zhang L, Yao R, Gao J, Tan D, Yang X, Wen M, et al. An integrated radiomics model incorporating diffusion-weighted imaging and 18F-FDG PET imaging improves the performance of differentiating glioblastoma from solitary brain metastases. Front Oncol. 2021;11:732704.
- Chiang IC, Kuo YT, Lu CY, Yeung KW, Lin WC, Sheu FO, et al. Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology. 2004;46(8):619-27.
- Pavlisa G, Rados M, Pavlisa G, Pavic L, Potocki K, Mayer D. The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. Clin Imaging. 2009;33(2):96-101.
- Rollin N, Guyotat J, Streichenberger N, Honnorat J, Tran Minh VA, Cotton F. Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology. 2006;48(3):150-9.
- Lee EJ, terBrugge K, Mikulis D, Choi DS, Bae JM, Lee SK, et al. Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol. 2011;196(1):71-6.
- Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol. 2007;25(26):4104-9.
Comparison of Diffusion MRI Findings of High-Graded Primary Brain Tumors and Metastatic Brain Tumors
Abstract
Aim: Glioblastomas are the highest grade and most mortal primary brain tumors. Cerebral masses that occur with the metastasis of cancers of tissues other than brain are included in the differential diagnosis of glioblastomas. This study aimed to compare the diffusion-weighted imaging signal characteristics of primary and metastatic brain masses and to describe the findings that may be useful in the differential diagnosis.
Material and Methods: Patients with pathologically diagnosed glioblastoma and patients with pathologically diagnosed metastases or radiologically diagnosed brain metastases were included in the study. Diffusion-weighted imaging signal properties in magnetic resonance imaging examinations obtained with a 1.5 Tesla scanner were retrospectively analyzed. The signal features and short and long diameters of the lesions were measured and compared in both patient groups.
Results: A total of 54 patients, 24 glioblastomas, and 30 brain metastases were included in the study. The most common signal feature of diffusion-weighted imaging in the glioblastoma group was heterogeneous hyper- and hypointense areas observed in 20 (83.3%) patients. The most common signal feature in the metastasis group was the peripheral hyperintense ring and central hypointense signal in 16 (53.3%) patients. There was no significant relation found between the number of lesions and the primary brain tumor and metastases.
Conclusion: Although only signal characteristics are used without quantitative assessment in diffusion-weighted imaging, it may be helpful in the differential diagnosis of primary and metastatic brain masses. It is important to remember that the masses in the two groups can have comparable signal properties.
References
- Lapointe S, Perry A, Butowski NA. Primary brain tumors in adults. Lancet. 2018;392(10145):432-46.
- Mourad AF, Mohammad HEG, Sayed MM, Ragae MA. What’s the clinical significance of adding diffusion and perfusion MRI in the differentiation of glioblastoma multiforme and solitary brain metastasis? Egypt J Radiol Nucl Med. 2017;48(3):661-9.
- Yazol M, Öner AY. Magnetic resonance imaging in brain gliomas. Trd Sem. 2016;4(1):20-36. Turkish.
- Xiang C, Chen Q, Zha Y. Specific features of primary central nervous system lymphoma in comparison with glioblastoma on conventional MRI. Iran J Radiol. 2019;16(1):e78868.
- Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-weighted imaging: recent advances and applications. Semin Ultrasound CT MR. 2021;42(5):490-506.
- Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, et al. The role of diffusion-weighted imaging in patients with brain tumors. Am J Neuroradiol. 2001;22(6):1081-8.
- Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J. MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol. 1994;162(3):671-7.
- Yan Q, Li F, Cui Y, Wang Y, Wang X, Jia W, et al. Discrimination between glioblastoma and solitary brain metastasis using conventional MRI and diffusion-weighted imaging based on a deep learning algorithm. J Digit imaging. 2023;36(4):1480-8.
- Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I, et al. Machine learning for semi-automated classification of glioblastoma, brain metastasis, and central nervous system lymphoma using magnetic resonance advanced imaging. Ann Transl Med. 2019;7(11):232.
- Zhang L, Yao R, Gao J, Tan D, Yang X, Wen M, et al. An integrated radiomics model incorporating diffusion-weighted imaging and 18F-FDG PET imaging improves the performance of differentiating glioblastoma from solitary brain metastases. Front Oncol. 2021;11:732704.
- Chiang IC, Kuo YT, Lu CY, Yeung KW, Lin WC, Sheu FO, et al. Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology. 2004;46(8):619-27.
- Pavlisa G, Rados M, Pavlisa G, Pavic L, Potocki K, Mayer D. The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. Clin Imaging. 2009;33(2):96-101.
- Rollin N, Guyotat J, Streichenberger N, Honnorat J, Tran Minh VA, Cotton F. Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology. 2006;48(3):150-9.
- Lee EJ, terBrugge K, Mikulis D, Choi DS, Bae JM, Lee SK, et al. Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol. 2011;196(1):71-6.
- Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol. 2007;25(26):4104-9.
Details
| Primary Language | English |
|---|---|
| Subjects | Radiology and Organ Imaging, Diagnostic Radiography |
| Journal Section | Research Article |
| Authors | |
| Early Pub Date | April 6, 2024 |
| Publication Date | April 30, 2024 |
| Submission Date | November 4, 2023 |
| Acceptance Date | March 17, 2024 |
| Published in Issue | Year 2024 Volume: 26 Issue: 1 |
Cite
Duzce Medical Journal is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.