High-Resolution Intratumoral Susceptibility Signal (ITSS) as an Adjunctive Imaging Tool in the Evaluation of Treatment Response of Brain Metastases Following Stereotactic Radiosurgery
Main Article Content
Abstract
Purpose: To evaluate the longitudinal change of intra-tumoral susceptibility signal (ITSS) on high-resolution SWI as an adjunctive imaging tool to evaluate treatment response of brain metastasis following stereotactic radiosurgery. This approach will allow further stratification of the patients and guide clinical decision making.
Methods: An IRB approved retrospective study included 63 brain metastatic lesions within 49 patients (33 females and 16 males) who have undergone stereotactic radiosurgery with at least one follow-up MRI and available clinical data. The average age was 63.17 years (±1.48, ranged from 34-83 years). The longitudinal change in ITSS was categorized into 3 groups; increased, stable and decreased. The treatment response of each lesion was evaluated according to the longitudinal change in size, enhancement and susceptibility at the baseline and follow-up MRIs. Chi-square test was used to compare differences in categorical variables. Receiver operating characteristics (ROC) curve was used to evaluate the accuracy of including longitudinal change in ITSS with size and enhancement pattern in determining the treatment response following SRS.
Results: Our results demonstrated higher sensitivity and specificity when including longitudinal change in ITSS with size and enhancement for the evaluation of the treatment response of brain metastatic lesions treated with SRS. There was statistically significant difference between the different ITSS and enhancement patterns at baseline and follow-up MRIs (Wilcoxon Signed Ranks Test (p = .000, and .003) respectively. The multiparametric analysis of the longitudinal change in size, contrast enhancement, and ITSS in the evaluation of treatment response in the follow-up MRIs, showed that the sensitivity and specificity significantly improved (AUC 0.953).
Conclusion: High resolution SWI can contribute as an imaging biomarker with supplemental information for monitoring treatment and predicting treatment response. High resolution SWI can complement the standard contrast enhanced T1 images to evaluate treatment response with a multiparametric MRI approach.
Article Details
How to Cite
NADA, Ayman et al.
High-Resolution Intratumoral Susceptibility Signal (ITSS) as an Adjunctive Imaging Tool in the Evaluation of Treatment Response of Brain Metastases Following Stereotactic Radiosurgery.
Medical Research Archives, [S.l.], v. 10, n. 6, june 2022.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/2807>. Date accessed: 04 dec. 2024.
doi: https://doi.org/10.18103/mra.v10i6.2807.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
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53. Kong, L.-W. et al. intratumoral Susceptibility Signals Reflect Biomarker Status in Gliomas. Scientific RepoRtS | 9, 17080 (2019).
2. Taunk, N. K. et al. Early posttreatment assessment of MRI perfusion biomarkers can predict long-term response of lung cancer brain metastases to stereotactic radiosurgery. Neuro-Oncology 20, 567–575 (2018).
3. Lohmann, P. et al. PET/MRI Radiomics in Patients With Brain Metastases. Frontiers in Neurology vol. 11 1 (2020).
4. Nayak, L., Lee, E. Q. & Wen, P. Y. Epidemiology of brain metastases. Current Oncology Reports 14, 48–54 (2012).
5. Kniep, H. C. et al. Radiomics of brain MRI: Utility in prediction of metastatic tumor type. Radiology 290, 479–487 (2019).
6. Lin, C. Y. et al. Brain MRI imaging characteristics predict treatment response and outcome in patients with de novo brain metastasis of EGFR-mutated NSCLC. Medicine (United States) 98, (2019).
7. Singh, C., Qian, J. M., Yu, J. B. & Chiang, V. L. Local tumor response and survival outcomes after combined stereotactic radiosurgery and immunotherapy in non-small cell lung cancer with brain metastases. Journal of Neurosurgery 132, 512–517 (2020).
8. Aoyama, H. et al. Stereotactic radiosurgery plus whole-brain radiation therapy vs stereotactic radiosurgery alone for treatment of brain metastases: A randomized controlled trial. J Am Med Assoc (2006) doi:10.1001/jama.295.21.2483.
9. Chang, E. L. et al. Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation: a randomised controlled trial. The Lancet Oncology (2009) doi:10.1016/S1470-2045(09)70263-3.
10. Lohmann, P. et al. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. NeuroImage: Clinical 20, 537–542 (2018).
11. Suh, J. H. et al. Current approaches to the management of brain metastases. Nature Reviews Clinical Oncology 17, 279–299 (2020).
12. Ong, W. L., Wada, M., Ruben, J., Foroudi, F. & Millar, J. Contemporary practice patterns of stereotactic radiosurgery for brain metastasis: A review of published Australian literature. Journal of Medical Imaging and Radiation Oncology 63, 711–720 (2019).
13. Rosenfelder, N. & Brada, M. Integrated treatment of brain metastases. Current Opinion in Oncology 31, 501–507 (2019).
14. Langen, K. J. & Galldiks, N. Update on amino acid pet of brain tumours. Current Opinion in Neurology (2018) doi:10.1097/WCO.0000000000000574.
15. Langen, K. J., Galldiks, N., Hattingen, E. & Shah, N. J. Advances in neuro-oncology imaging. Nature Reviews Neurology (2017) doi:10.1038/nrneurol.2017.44.
16. Lin, N. U. et al. Response assessment criteria for brain metastases: Proposal from the RANO group. The Lancet Oncology (2015) doi:10.1016/S1470-2045(15)70057-4.
17. Okada, H. et al. Immunotherapy response assessment in neuro-oncology: A report of the RANO working group. The Lancet Oncology (2015) doi:10.1016/S1470-2045(15)00088-1.
18. Halefoglu, A. M. & Yousem, D. M. Susceptibility weighted imaging: Clinical applications and future directions. World J Radiol 10, 30–45 (2018).
19. Roh, K., Kang, H. & Kim, I. Clinical Applications of Neuroimaging with Susceptibility Weighted Imaging: Review Article. Journal of the Korean Society of Magnetic Resonance in Medicine 18, 290 (2014).
20. Ruetten, P. P. R., Gillard, J. H. & Graves, M. J. introduction to Quantitative Susceptibility Mapping and Susceptibility weighted imaging. (2019).
21. Tate, D. F. et al. Susceptibility Weighted Imaging and White Matter Abnormality Findings in Service Members With Persistent Cognitive Symptoms Following Mild Traumatic Brain Injury. MILITARY MEDICINE 182, e1651–e1658 (2017).
22. Huang, Y.-L. et al. Susceptibility-weighted MRI in mild traumatic brain injury From the Department of Diagnostic Radiology (Y. (2015).
23. Bigler, E. D., Maxwell, W. L., Bigler, E. D. & Maxwell, W. L. Neuropathology of mild traumatic brain injury: relationship to neuroimaging findings. Brian Imaging and Behavior 6, 108–136 (2012).
24. Darwish, E. A. F., Abdelhameed-El-Nouby, M. & Geneidy, E. Mapping the ischemic penumbra and predicting stroke progression in acute ischemic stroke: the overlooked role of susceptibility weighted imaging. Insights into Imaging 11, 1–8 (2020).
25. Luo, S., Yang, L. & Luo, Y. Susceptibility-weighted imaging predicts infarct size and early-stage clinical prognosis in acute ischemic stroke. Neurological Sciences 39, 1049–1055 (2018).
26. Hsu, C. C.-T. et al. Susceptibility weighted imaging in acute cerebral ischemia: review of emerging technical concepts and clinical applications. Neuroradiol J 30, 109–119 (2017).
27. 790 Kim et al. Susceptibility changes in SAD Quantitative Magnetic Susceptibility Assessed by 7T Magnetic Resonance Imaging in Alzheimer’s Disease Caused by Streptozotocin Administration. Quantitative Imaging in Medicine and Surgery 10, 789–797 (2020).
28. O’callaghan, J. et al. Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer’s disease. Neuroimage 159, 334–345 (2017).
29. Acosta-Cabronero, J. et al. In Vivo Quantitative Susceptibility Mapping (QSM) in Alzheimer’s Disease. PLoS ONE 8, e81093–e81108 (2013).
30. Kim, H.-G. et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease ☆. NeuroImage: Clinical 16, 429–438 (2017).
31. Li, D. T. H. et al. Quantitative susceptibility mapping as an indicator of subcortical and limbic iron abnormality in Parkinson’s disease with dementia. NeuroImage: Clinical 20, 365–373 (2018).
32. Belliveau, J. G. et al. Apparent transverse relaxation (R2∗) on MRI as a method to differentiate treatment effect (pseudoprogression) versus progressive disease in chemoradiation for malignant glioma. Journal of Medical Imaging and Radiation Oncology 62, 224–231 (2018).
33. Prediction of radiation necrosis in a rodent model using magnetic resonance imaging apparent transverse relaxation ( ). Physics in Medicine & Biology 63, 035010 (9pp) (2018).
34. Jenrow, K. A. et al. Selective Inhibition of Microglia-Mediated Neuroinflammation Mitigates Radiation-Induced Cognitive Impairment. (2013) doi:10.1667/RR3026.1.
35. Radbruch, A. et al. Differentiation of brain metastases by percentagewise quantification of intratumoral-susceptibility-signals at 3 Tesla. European Journal of Radiology 81, 4064–4068 (2012).
36. Nivet, A., Schlienger, M., Clavère, P. & Huguet, F. Effects of high-dose irradiation on vascularization: Physiopathology and clinical consequences. Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique 23, 161–167 (2019).
37. Adi Vachha, B. et al. Clinical Value of Susceptibility Weighted Imaging of Brain Metastases. Frontiers in Neurology | www.frontiersin.org 11, 55 (2020).
38. Kang, H. & Jang, S. The diagnostic value of postcontrast susceptibility-weighted imaging in the assessment of intracranial brain neoplasm at 3T. Acta Radiologica (2020) doi:10.1177/0284185120940265.
39. Schwarz, D. et al. Susceptibility-weighted imaging in malignant melanoma brain metastasis. Journal of Magnetic Resonance Imaging 50, 1251–1259 (2019).
40. Silbergeld, D., Fink, K. R. & Fink, J. R. Surgical Neurology International Surgical Neurology International SNI: Neuro-Oncology, a supplement to Surgical Neurology International OPEN ACCESS Imaging of brain metastases. doi:10.4103/2152-7806.111298.
41. Huang, S. Y. et al. Advanced Imaging of Brain Metastases: From Augmenting Visualization and Improving Diagnosis to Evaluating Treatment Response. Frontiers in Neurology | www.frontiersin.org 1, 270–284 (2020).
42. Niranjan, A., Dade Lunsford, L. & Ahluwalia, M. S. Targeted therapies for brain metastases. Progress in Neurological Surgery 34, 125–137 (2019).
43. Masoud, V. & Pagès, G. Targeted therapies in breast cancer: New challenges to fight against resistance World Journal of Clinical Oncology. World J Clin Oncol 8, 120–134 (2017).
44. Pope, W. B. Brain metastases: neuroimaging. Handb Clin Neurol 149, 89–112 (2018).
45. Liu, Q., Tong, X. & Wang, J. Management of brain metastases: history and the present. Chinese Neurosurgical Journal 5, 1–8 (2019).
46. di Ieva, A. et al. Advanced Magnetic Resonance Imaging Techniques in Management of Brain Metastases. Front. Oncol 9, 440–456 (2019).
47. Lee, J., Hirano, Y., Fukunaga, M., Silva, A. C. & Duyn, J. H. On the contribution of deoxy-hemoglobin to MRI gray-white matter phase contrast at high field. Neuroimage 49, 193–198 (2010).
48. Sawlani, V. et al. Evaluation of Response to Stereotactic Radiosurgery in Brain Metastases Using Multiparametric Magnetic Resonance Imaging and a Review of the Literature. Clinical Oncology 31, 41–49 (2019).
49. Pinker, K. et al. High-Resolution Contrast-Enhanced, Susceptibility-Weighted MR Imaging at 3T in Patients with Brain Tumors: Correlation with Positron-Emission Tomography and Histopathologic Findings. doi:10.3174/ajnr.A0540.
50. Al, A. et al. Distinguishing Recurrent Primary Brain Tumor from Radiation Injury: A Preliminary Study Using a Susceptibility-Weighted MR ImagingGuided Apparent Diffusion Coefficient Analysis Strategy. doi:10.3174/ajnr.A2011.
51. Gasparotti, R., Pinelli, L. & Liserre, R. New MR sequences in daily practice: susceptibility weighted imaging. A pictorial essay. Insights into Imaging 2, 335 (2011).
52. Mittal, S., Wu, Z., Neelavalli, J. & Haacke, E. M. Susceptibility-weighted imaging: Technical aspects and clinical applications, part 2. American Journal of Neuroradiology vol. 30 232–252 (2009).
53. Kong, L.-W. et al. intratumoral Susceptibility Signals Reflect Biomarker Status in Gliomas. Scientific RepoRtS | 9, 17080 (2019).