Susceptibility weighted imaging for glioma

Susceptibility weighted imaging for glioma

Gradient echo T2WI MRI is the 3–4 × more sensitive test than FLAIR for demonstrating intraparenchymablood (which appears dark) due to high sensitivity to paramagnetic artifact. It is not as sensitive as SWI.

Susceptibility weighted imaging (SWI) of brain tumors provides information about neoplastic vasculature and intratumoral micro- and macrobleedings. Low- and high-grade gliomas can be distinguished by SWI due to their different vascular characteristics. Fractal analysis allows for quantification of these radiological differences by a computer-based morphological assessment of SWI patterns.

SWI and CE-SWI are indispensable tools for diagnosis, preoperative grading, posttherapy surveillance, and assessment of glioma 1).

The theory that susceptibility signals show microvasculature that correlates with tumor grade has been well validated with the help of various studies. However, the cons of SWI lie within the technique itself. Small tweaks made in imaging parameters lead to varying subjective results. This lack of standardization of the SWI technique remains an obstacle in its integration into mainstream grading of gliomas. SWI for now plays an important role in detecting gliomas and guiding biopsies. The goal of noninvasive accurate grading of tumors is yet to be realized. Further studies with greater sample size and better collaborations are warranted in this regard 2).

Eighteen GBM patients were retrospectively analyzed. After completion of therapy, imaging was performed every 3 months. MRI was analyzed at the following time points: after the third and sixth cycle of adjuvant temozolomide chemotherapy, thereafter in 3 month intervals and at recurrence. The number of SWI positive tumor pixels was quantified and compared with progression as defined by the RANO criteria on T2- and contrast-enhanced T1-weighted MRI sequences (T1-CE).

The MRI interval between completion of the sixth chemotherapy cycle and last MRI before progression was 390 ± 292 days. Between the last MRI before progression and at progression a significant increase in SWI positive tumor pixels was observed (P = .012), whereas tumor size remained unchanged (RANO T2: P = .385; RANO T1-CE: P = .165). The number of SWI positive pixels remained unchanged between last MRI before progression until progression (P = .149), whereas RANO T2 and T1-CE showed tumor progression (interval 128 ± 69 days).

SWI positive pixel count increases significantly prior to changes in tumor size (RANO). The findings may be explained by microbleeds compatible with stimulation of angiogenesis and possibly serve as an early biomarker of tumor progression 3).

Seventy-eight patients affected by brain tumors of different histopathology (low- and high-grade gliomas, metastases, meningiomas, lymphomas) were included. All patients underwent preoperative 3-T magnetic resonance imaging including SWI, on which the lesions were contoured. The images underwent automated computation, extracting 2 quantitative parameters: the volume fraction of SWI signals within the tumors (signal ratio) and the morphological self-similar features (fractal dimension [FD]). The results were then correlated with each histopathological type of tumor.

Signal ratio and FD were able to differentiate low-grade gliomas from grade III and IV gliomas, metastases, and meningiomas (P < .05). FD was statistically different between lymphomas and high-grade gliomas (P < .05). A receiver-operating characteristic analysis showed that the optimal cutoff value for differentiating low- from high-grade gliomas was 1.75 for FD (sensitivity, 81%; specificity, 89%) and 0.03 for signal ratio (sensitivity, 80%; specificity, 86%).

FD of SWI on 3-T magnetic resonance imaging is a novel image biomarker for glioma grading and brain tumor characterization. Computational models offer promising results that may improve diagnosis and open perspectives in the radiological assessment of brain tumors 4).



Hsu CC, Watkins TW, Kwan GN, Haacke EM. Susceptibility-Weighted Imaging of Glioma: Update on Current Imaging Status and Future Directions. J Neuroimaging. 2016 Jul;26(4):383-90. doi: 10.1111/jon.12360. Epub 2016 May 26. Review. PubMed PMID: 27227542.

Mohammed W, Xunning H, Haibin S, Jingzhi M. Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review. Cancer Imaging. 2013 Apr 24;13:186-95. doi: 10.1102/1470-7330.2013.0020. Review. PubMed PMID: 23618919; PubMed Central PMCID: PMC3636597.

van Leyen K, Roelcke U, Gruber P, Remonda L, Berberat J. Susceptibility and Tumor Size Changes During the Time Course of Standard Treatment in Recurrent Glioblastoma. J Neuroimaging. 2019 May 21. doi: 10.1111/jon.12631. [Epub ahead of print] PubMed PMID: 31112344.

Di Ieva A, Le Reste PJ, Carsin-Nicol B, Ferre JC, Cusimano MD. Diagnostic Value of Fractal Analysis for the Differentiation of Brain Tumors Using 3-Tesla Magnetic Resonance Susceptibility-Weighted Imaging. Neurosurgery. 2016 Dec;79(6):839-846. PubMed PMID: 27332779.

Intracranial Glioma Workshop

The course is aiming at covering all aspects of gliomas, from their genetic analysis and profiling to the most advanced surgical resective techniques and the long-term follow-up and support of these patients. An astonishing multi-national faculty, including leading figures in the field of neuro-oncology constitutes a guarantee that the participants will be able to familiarize themselves with all advanced imaging and surgical techniques, utilized for the most efficient and the safest management of patients with intracranial gliomas. A well-balanced combination of vivid presentations, stimulating debates, highly-didactic video sessions, and practical hands-on will ensure that each participant will be exposed to all diagnostic methodologies, to the most updated surgical strategies and to the most recent adjuvant treatment protocols. Moreover, the interactive e-poster session will serve as a forum for exchanging bright new ideas regarding basic science, translational, and clinical glioma research.

The event is hosted in Athens, at the Royal Olympic Hotel, situated in the heart of the city center and at walking distance from many important historical sights, such as the new Acropolis Museum, Hadrian’s Gate, the Temple of Zeus and the National Gardens, as well as the main shopping district of the city.

FET PET for Low Grade Glioma

FET PET for Low Grade Glioma

Positron emission tomography (PET) imaging using amino acid tracers has in recent years become widely used in the diagnosis and prediction of disease course in diffuse low grade gliomas (LGG). However, implications of preoperative PET for treatment and prognosis in this patient group have not been systematically studied.

The aim of a systematic review was to evaluate the preoperative diagnostic and prognostic value of amino acid PET in suspected diffuse LGG. Medline, Cochrane Library, and Embase databases were systematically searched using keywords “PET,” “low grade glioma,” and “amino acids tracers” with their respective synonyms. Out of 2137 eligible studies, 28 met the inclusion criteria. Increased amino acid uptake (lesion/brain) was consistently reported among included studies; in 25-92% of subsequently histopathology-verified LGG, in 83-100% of histopathology-verified HGG, and also in some non-neoplastic lesions. No consistent results were found in studies reporting hot spot areas on PET in MRI-suspected LGG. Thus, the diagnostic value of amino acid PET imaging in suspected LGG has proven difficult to interpret, showing clear overlap and inconsistencies among reported results. Similarly, the results regarding the prognostic value of PET in suspected LGG and the correlation between uptake ratios and the molecular tumor status of LGG were conflicting. This systematic review illustrates the difficulties with prognostic studies presenting data on group-level without adjustment for established clinical prognostic factors, leading to a loss of additional prognostic information.

Näslund et al., conclude that the prognostic value of PET is limited to analysis of histological subgroups of LGG and is probably strongest when using kinetic analysis of dynamic FET uptake parameters 1).

For Chan et al., FET PET demonstrated a high positive predictive value for glioma in patients with indeterminate brain lesions on MRI. The combination of negative FET and negative FDG PET scans may predict an indolent clinical course. Confirmatory trials are needed to establish the potential value of FET PET in guiding surgical management in this cohort 2).

Sixty-one patients harboring Gd-negative WHO grade II or III glioma receiving alkylating agents (temozolomide or CCNU/procarbacine) were included. All patients underwent MRI and 18F-FET-PET before chemotherapy and 6 months later. They calculated T2-volume, 18F-FET-PET based biological tumour volume (BTV) and maximal tumour-to-brain ratio (TBRmax). Moreover, dynamic PET acquisition was performed using time-activity-curves (TACs) analysis. For MRI-based response assessment, RANO criteria for low-grade glioma were used. For 18F-FET-PET, following classification scheme was tested: responsive disease (RD) when a decrease in either BTV ≥ 25% and/or TBRmax ≥ 10% occurred, an increase in BTV ≥ 25% and/or TBRmax increase > 10% characterized progressive disease (PD), minor changes ± 25% for BTV and ± 10% for TBRmax were regarded as stable disease (SD). Post-chemotherapy survival (PCS) and time-to-treatment failure (TTF) were calculated using the Kaplan-Meier method.

18F-FET-PET based response has shown patients with RD to have the longest TTF time (78.5 vs 24.6 vs 24.1 months, p = 0.001), while there was no significant difference between patients with a SD and PD. A comparable pattern was observed for PCS (p < 0.001). T2-volume based assessment was not associated with outcome.

18F-FET-PET is a promising biomarker for early response assessment in Gd-negative gliomas undergoing chemotherapy. It might be helpful for a timely adjustment of potentially ineffective treatment concepts and overcomes limitations of conventional structural imaging 3).

A single FET PET scan obtained at the time of radiological and/or clinical progression seems to be of limited value in distinguishing transformed from nontransformed low grade gliomas (LGGs), especially if knowledge of the primary tumor histopathology is not known. Therefore, FET PET imaging alone is not adequate to replace histological confirmation, but it may provide valuable information on the location and delineation of active tumor tissue, as well as an assessment of tumor biology in a subgroup of LGGs 4).

Both Magnetic resonance perfusion imaging and FET PET provide grading information in cerebral gliomas.

Seventy-two patients with untreated gliomas [22 low-grade gliomas (LGG), and 50 high-grade gliomas (HGG)] were investigated with 18F-FET PET and Magnetic resonance perfusion imaging using a hybrid PET/MR scanner. After visual inspection of PET and Magnetic resonance perfusion imaging maps (rCBV, rCBF, MTT), volumes of interest (VOIs) with a diameter of 16 mm were centered upon the maximum of abnormality in the tumor area in each modality and the contralateral unaffected hemisphere. Mean and maximum tumor-to-brain ratios (TBRmean, TBRmax) were calculated. In addition, Time-to-Peak (TTP) and slopes of time-activity curves were calculated for 18F-FET PET. Diagnostic accuracies of 18F-FET PET and Magnetic resonance perfusion imaging for differentiating low-grade glioma (LGG) from high-grade glioma (HGG) were evaluated by receiver operating characteristic analyses (area under the curve; AUC).

The diagnostic accuracy of 18F-FET PET and Magnetic resonance perfusion imaging to discriminate LGG from HGG was similar with highest AUC values for TBRmean and TBRmax of 18F-FET PET uptake (0.80, 0.83) and for TBRmean and TBRmax of rCBV (0.80, 0.81). In case of increased signal in the tumor area with both methods (n = 32), local hot-spots were incongruent in 25 patients (78%) with a mean distance of 10.6 ± 9.5 mm. Dynamic FET PET and combination of different parameters did not further improve diagnostic accuracy.

Both 18F-FET PET and Magnetic resonance perfusion imaging discriminate LGG from HGG with similar diagnostic performance. Regional abnormalities in the tumor area are usually not congruent indicating that tumor grading by 18F-FET PET and Magnetic resonance perfusion imaging is based on different pathophysiological phenomena 5).

Fifty-nine patients with newly diagnosed low-grade glioma and dynamic (18)F-FET PET before histopathologic assessment were retrospectively investigated. (18)F-FET PET analysis comprised a qualitative visual classification of lesions; assessment of the semiquantitative parameters maximal, mean, and total standardized uptake value as ratio to background and biologic tumor volume; and dynamic analysis of intratumoral (18)F-FET uptake over time (increasing vs. decreasing time-activity curves). The correlation between PET parameters and progression-free survival, overall survival, and time to malignant transformation was investigated.

(18)F-FET uptake greater than the background level was found in 34 of 59 tumors. Dynamic (18)F-FET uptake analysis was available for 30 of these 34 patients. Increasing and decreasing time-activity curves were found in 18 and 12 patients, respectively. Neither the qualitative factor presence or absence of (18)F-FET uptake nor any of the semiquantitative uptake parameters significantly influenced clinical outcome. In contrast, decreasing time-activity curves in the kinetic analysis were highly prognostic for shorter progression-free survival and time to malignant transformation (P < 0.001).

Absence of (18)F-FET uptake in newly diagnosed astrocytic low-grade glioma does not generally indicate an indolent disease course. Among the (18)F-FET-positive gliomas, decreasing time-activity curves in dynamic (18)F-FET PET constitute an unfavorable prognostic factor in astrocytic low-grade glioma and, by identifying high-risk patients, may ease treatment decisions 6).



Näslund O, Smits A, Förander P, Laesser M, Bartek J Jr, Gempt J, Liljegren A, Daxberg EL, Jakola AS. Amino acid tracers in PET imaging of diffuse low-grade gliomas: a systematic review of preoperative applications. Acta Neurochir (Wien). 2018 Jul;160(7):1451-1460. doi: 10.1007/s00701-018-3563-3. Epub 2018 May 24. Review. PubMed PMID: 29797098; PubMed Central PMCID: PMC5995993.

Chan DL, Hsiao E, Schembri G, Bailey DL, Roach PJ, Lee A, Jayamanne D, Ghasemzadeh M, Hayes A, Cook R, Parkinson J, Drummond JP, Ibbett I, Wheeler HR, Back M. FET PET in the evaluation of indeterminate brain lesions on MRI: Differentiating glioma from other non-neoplastic causes – A pilot study. J Clin Neurosci. 2018 Dec;58:130-135. doi: 10.1016/j.jocn.2018.09.009. Epub 2018 Sep 19. PubMed PMID: 30243602.

Suchorska B, Unterrainer M, Biczok A, Sosnova M, Forbrig R, Bartenstein P, Tonn JC, Albert NL, Kreth FW. (18)F-FET-PET as a biomarker for therapy response in non-contrast enhancing glioma following chemotherapy. J Neurooncol. 2018 Sep;139(3):721-730. doi: 10.1007/s11060-018-2919-0. Epub 2018 Jun 8. PubMed PMID: 29948765.

Bashir A, Brennum J, Broholm H, Law I. The diagnostic accuracy of detecting malignant transformation of low-grade glioma using O-(2-[18F]fluoroethyl)-l-tyrosine positron emission tomography: a retrospective study. J Neurosurg. 2018 Apr 1:1-14. doi: 10.3171/2017.8.JNS171577. [Epub ahead of print] PubMed PMID: 29624154.

Verger A, Filss CP, Lohmann P, Stoffels G, Sabel M, Wittsack HJ, Kops ER, Galldiks N, Fink GR, Shah NJ, Langen KJ. Comparison of (18)F-FET PET and perfusion-weighted MRI for glioma grading: a hybrid PET/MR study. Eur J Nucl Med Mol Imaging. 2017 Dec;44(13):2257-2265. doi: 10.1007/s00259-017-3812-3. Epub 2017 Aug 22. PubMed PMID: 28831534.

Jansen NL, Suchorska B, Wenter V, Eigenbrod S, Schmid-Tannwald C, Zwergal A, Niyazi M, Drexler M, Bartenstein P, Schnell O, Tonn JC, Thon N, Kreth FW, la Fougère C. Dynamic 18F-FET PET in newly diagnosed astrocytic low-grade glioma identifies high-risk patients. J Nucl Med. 2014 Feb;55(2):198-203. doi: 10.2967/jnumed.113.122333. Epub 2013 Dec 30. PubMed PMID: 24379223.
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