Glioblastoma Pseudoprogression Diagnosis [Neurosurgery Wiki]

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====== Glioblastoma Pseudoprogression Diagnosis ======

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1Vu-RtW34K2UuP9iEWm81xXFTRREX5PtKi2bvGW34UplA-3g7l/?limit=15&utm_campaign=pubmed-2&fc=20231205121852}}

While [[pathologic diagnosis]] is the [[gold standard]] to differentiate true [[progression]] and [[pseudoprogression]], the lack of objective clinical [[standard]]s and admixed histologic presentation creates the need to (1) validate the accuracy of current approaches and (2) characterize differences between these entities to objectively differentiate true disease. 

Wang et al. demonstrated using an [[online]] [[RNA sequencing]] [[repository]] of [[recurrent glioblastoma]] samples that cancer-immune cell activity levels correlate with heterogeneous clinical outcomes in patients. Furthermore, nCounter RNA expression analysis of 48 clinical samples taken from a second neurosurgical resection supports that pseudoprogression [[gene expression]] pathways are dominated by [[immune activation]], whereas progression is predominated by [[cell cycle]] activity. Automated image processing and spatial expression analysis however highlight a failure to apply these broad expressional differences in a subset of cases with clinically challenging admixed histology. Encouragingly, applying unsupervised clustering approaches over segmented histologic images provides a novel understanding of morphologically derived differences between progression and pseudoprogression. Spatially derived data further highlighted polarization of myeloid populations that may underscore the tumorgenicity of novel lesions. These findings not only help provide further clarity of potential targets for pathologists to better assist stratification of progression and pseudoprogression but also highlight the evolution of tumor-immune [[microenvironment]] changes that promote tumor recurrence
((Wang W, Tugaoen JD, Fadda P, Toland AE, Ma Q, Elder JB, Giglio P; James Cancer Center Integrated Neuro-Oncology Team; Otero JJ. Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences. Acta Neuropathol Commun. 2023 Dec 4;11(1):192. doi: 10.1186/s40478-023-01587-w. PMID: 38049893.)).
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With conventional MRI, recurrences often have similar radiologic characteristics as therapy-related changes such as pseudoprogression (PsP) or [[radionecrosis]], and its mutual differentiation remains challenging
(( Brandsma D., Stalpers L., Taal W., Sminia P., van den Bent M. J. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. The Lancet Oncology. 2008;9(5):453–461. doi: 10.1016/s1470-2045(08)70125-6)).

Modern multiparametric MRI techniques such as [[diffusion weighted imaging]] (DWI) with [[apparent diffusion coefficient]] (ADC) mapping, [[dynamic susceptibility-weighted contrast-enhanced perfusion imaging]], and [[MR spectroscopy]] (MRS) allow a much deeper and still noninvasive insight into interpretation of brain lesions, resulting in greater specificity of diagnostic imaging, especially in combination with [[PET with radiolabeled aminoacid]]
((Kao H.-W., Chiang S.-W., Chung H.-W., Tsai F. Y., Chen C.-Y. Advanced MR imaging of gliomas: an update. BioMed Research International. 2013;2013:14. doi: 10.1155/2013/970586.970586))
((Bulik M., Jancalek R., Vanicek J., Skoch A., Mechl M. Potential of MR spectroscopy for assessment of glioma grading. Clinical Neurology and Neurosurgery. 2013;115(2):146–153. doi: 10.1016/j.clineuro.2012.11.002))
((Roy B., Gupta R. K., Maudsley A. A., et al. Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology. 2013;55(5):603–613. doi: 10.1007/s00234-013-1145-x)). 
((Ahmed R., Oborski M. J., Hwang M., Lieberman F. S., Mountz J. M. Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods. Cancer Management and Research. 2014;6(1):149–170. doi: 10.2147/cmar.s54726)).
((Ion-Margineanu A., van Cauter S., Sima D. M., et al. Tumour relapse prediction using multiparametric MR data recorded during follow-up of Glioblastoma patients. BioMed Research International. In press)).

However, in routine practice, availability of advanced MRI as well as PET methods is limited with exception of DWI/ADC and MRS. DWI reflects changes in water diffusion as a result of changed tissue microarchitecture due to tumor infiltration and can be quantitatively assessed with the ADC. MRS enables noninvasive examination of the spatial distribution of multiple metabolite concentrations in normal and pathological tissues.

ADCmean values ≤ 1300 × 10−6 mm2/s and tCho/tNAA ratio ≥ 1.4 are strongly associated with differentiating Glioblastoma recurrence from treatment-related changes indicative of PsP.

Institutional validation of cut-off values obtained from advanced MRI methods is warranted not only for diagnosis of Glioblastoma recurrence, but also as enrollment criteria in salvage clinical trials and for reporting of outcomes of initial treatment
((Bulik M, Kazda T, Slampa P, Jancalek R. The Diagnostic Ability of Follow-Up
Imaging Biomarkers after Treatment of Glioblastoma in the Temozolomide Era:
Implications from Proton MR Spectroscopy and Apparent Diffusion Coefficient
Mapping. Biomed Res Int. 2015;2015:641023. doi: 10.1155/2015/641023. Epub 2015
Sep 13. PubMed PMID: 26448943; PubMed Central PMCID: PMC4584055.)).

Surgical sampling and histologic review of MRI changes after chemoRT may not serve as a gold standard to distinguish psPD from true progression in Glioblastoma patients. Refinement of the histological criteria, careful intraoperative selection of regions of interest and advanced imaging modalities are needed for early differentiation of PsPD from progression to guide clinical management
((Melguizo-Gavilanes I, Bruner JM, Guha-Thakurta N, Hess KR, Puduvalli VK.
Characterization of pseudoprogression in patients with glioblastoma: is histology
the gold standard? J Neurooncol. 2015 May;123(1):141-50. doi:
10.1007/s11060-015-1774-5. Epub 2015 Apr 18. PubMed PMID: 25894594.)).

===Dynamic susceptibility weighted contrast enhanced perfusion imaging===
Patients with pseudoprogression (n = 13) had Vp (mean) = 2.4 and Vp (90 %tile) = 3.2; and Ktrans (mean) = 3.5 and Ktrans (90 %tile) = 4.2. Patients with tumor progression (n = 24) had Vp (mean) = 5.3 and Vp (90 %tile) = 6.6; and Ktrans (mean) = 7.4 and Ktrans (90 %tile) = 9.1. Compared with tumor progression, pseudoprogression demonstrated lower Vp perfusion values (p = 0.0002) with a Vp (mean) cutoff <3.7 yielding 85 % sensitivity and 79 % specificity for pseudoprogression. Ktrans (mean) of >3.6 had a 69 % sensitivity and 79 % specificity for disease progression. DCE MRI shows lower plasma volume and time dependent leakage constant values in pseudoprogression than in tumor progression. A cut-off value with high sensitivity for pseudoprogression can be applied to aid in interpretation of DCE MRI
((Thomas AA, Arevalo-Perez J, Kaley T, Lyo J, Peck KK, Shi W, Zhang Z, Young RJ.
Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from 
recurrent glioblastoma. J Neurooncol. 2015 Oct;125(1):183-90. doi:
10.1007/s11060-015-1893-z. Epub 2015 Aug 15. PubMed PMID: 26275367.)).