Histogram analysis of 11C-methionine integrated PET/MRI may facilitate to determine the O6-methylguanylmethyltransferase methylation status in gliomas

Yu, Peng; Ning, Jing; Xu, Baixuan; Liu, Jiajin; Dang, Haodan; Mu, Liangshan; Xiao, Feng; Grimm, Robert; Tian, Jiahe

doi:10.1097/mnm.0000000000001039

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…It is reasonable to hypothesize that the more heterogeneous the tumor MET PET imaging, the more likely IDH status is to be wildtype. 11 C-MET PET played a significant role in evaluating the O 6methylguanylmethyltransferase methylation (MGMT) status in gliomas (27,28). PET imaging was suggested to be informative for preoperatively differentiating gliomas according to 2016 WHO classification, particularly for differentiating IDH-wildtype and IDH-mutant tumors (19).…”

Section: Discussionmentioning

confidence: 99%

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

Zhou

Wen

et al. 2020

Front. Oncol.

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Purpose: We developed a 11 C-Methionine positron emission tomography/computed tomography ( 11 C-MET PET/CT)-based nomogram model that uses easy-accessible imaging and clinical features to achieve reliable non-invasive isocitrate dehydrogenase (IDH)-mutant prediction with strong clinical translational capability. Methods: One hundred and ten patients with pathologically proven glioma who underwent pretreatment 11 C-MET PET/CT were retrospectively reviewed. IDH genotype was determined by IDH1 R132H immunohistochemistry staining. Maximum, mean and peak tumor-to-normal brain tissue (TNRmax, TNRmean, TNRpeak), metabolic tumor volume (MTV), total lesion methionine uptake (TLMU), and standard deviation of SUV (SUV SD ) of the lesions on MET PET images were obtained via a dedicated workstation (Siemens. syngo.via). Univariate and multivariate logistic regression models were used to identify the predictive factors for IDH mutation. Nomogram and calibration plots were further performed. Results: In the entire population, TNRmean, TNRmax, TNRpeak, and SUV SD of IDH-mutant glioma patients were significantly lower than these values of IDH wildtype. Receiver operating characteristic (ROC) analysis suggested SUV SD had the best performance for IDH-mutant discrimination (AUC = 0.731, cut-off ≤ 0.29, p < 0.001). All pairs of the 11 C-MET PET metrics showed linear associations by Pearson correlation coefficients between 0.228 and 0.986. Multivariate analyses demonstrated that SUV SD (>0.29 vs. ≤ 0.29 OR: 0.053, p = 0.010), dichotomized brain midline structure involvement (no vs. yes OR: 26.52, p = 0.000) and age (≤ 45 vs. >45 years OR: 3.23, p = 0.023), were associated with a higher incidence of IDH mutation. The nomogram modeling showed good discrimination, with a C-statistics of 0.866 (95% CI: 0.796–0.937) and was well-calibrated. Conclusions: 11 C-Methionine PET/CT imaging features (SUV SD and the involvement of brain midline structure) can be conveniently used to facilitate the pre-operative prediction of IDH genotype. The nomogram model based on 11 C-Methionine PET/CT and clinical age features might be clinically useful in non-invasive IDH mutation status prediction for untreated glioma patients.

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Section: Discussionmentioning

confidence: 99%

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

Zhou

Wen

et al. 2020

Front. Oncol.

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“…A total of 23 of the 220 studies focused on oligodendrogliomas, gliomas, and glioblastomas [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] employing several PET tracers: 11C-Metionine (n = 7), 18F-FET (n = 7), 18F-FDG (n = 4), 18F-FDOPA (n = 3), 18F-FLT (n = 1), 18F-FMISO (n = 1). These studies included an average of 78.4 patients (range: 32–160), 6/23 (26.1%) including more than 100 patients, 3/23 (13.0%) with prospective data and 8/23 (34.8%) with validation sets.…”

Section: Resultsmentioning

confidence: 99%

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

et al. 2022

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Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

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“…In this context, MET is a popular amino acid PET tracer that has relatively low uptake in normal brain tissue, and thus provides better detection of brain tumors (vs. FDG). Yu et al have reported that the histogram features from MET PET can help detect O6-methylguanylmethyltransferase (MGMT) methylation, which can help predict the response to temozolomide-based chemotherapy [19]. Zhao et al also reported that texture features from MET PET were able to help identify low-grade gliomas (IDH mutant and 1p/19q-codeleted), which have the most favorable outcomes [20].…”

Section: Discussionmentioning

confidence: 99%

Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma

et al. 2021

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Background: Positron emission tomography with 11C-methionine (MET) is well established in the diagnostic work-up of malignant brain tumors. Texture analysis is a novel technique for extracting information regarding relationships among surrounding voxels, in order to quantify their inhomogeneity. This study evaluated whether the texture analysis of MET uptake has prognostic value for patients with glioma. Methods: We retrospectively analyzed adults with glioma who had undergone preoperative metabolic imaging at a single center. Tumors were delineated using a threshold of 1.3-fold of the mean standardized uptake value for the contralateral cortex, and then processed to calculate the texture features in glioma. Results: The study included 42 patients (median age: 56 years). The World Health Organization classifications were grade II (7 patients), grade III (17 patients), and grade IV (18 patients). Sixteen (16.1%) all-cause deaths were recorded during the median follow-up of 18.8 months. The univariate analyses revealed that overall survival (OS) was associated with age (hazard ratio (HR) 1.04, 95% confidence interval (CI) 1.01–1.08, p = 0.0093), tumor grade (HR 3.64, 95% CI 1.63–9.63, p = 0.0010), genetic status (p < 0.0001), low gray-level run emphasis (LGRE, calculated from the gray-level run-length matrix) (HR 2.30 × 1011, 95% CI 737.11–4.23 × 1019, p = 0.0096), and correlation (calculated from the gray-level co-occurrence matrix) (HR 5.17, 95% CI 1.07–20.93, p = 0.041). The multivariate analyses revealed OS was independently associated with LGRE and correlation. The survival curves were also significantly different (both log-rank p < 0.05). Conclusion: Textural features obtained using preoperative MET positron emission tomography may compliment the semi-quantitative assessment for prognostication in glioma cases.

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Histogram analysis of 11C-methionine integrated PET/MRI may facilitate to determine the O6-methylguanylmethyltransferase methylation status in gliomas

Cited by 12 publications

References 37 publications

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

Radiomics in Oncological PET Imaging: A Systematic Review—Part 1, Supradiaphragmatic Cancers

Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma

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