11C-Methionine Integrated PET/MRI-Based Texture Analysis Features May Have a Potential Ability to Distinguish Oligodendroglioma (IDH-Mutant and 1p/19q-Codeleted) From Varied Gliomas

[PET/CT with 11C-methionine in assessment of brain glioma metabolism]

OBJECTIVE

To study 11C-methionine (MET) metabolism in gliomas using CNS tumor biobank imaging data.

MATERIAL AND METHODS

MRI and 11C-MET PET/CT were performed in 225 patients (49±14 years, M/F=84/101) according to standard protocols with analysis of 11C-MET accumulation index and volumetric parameters (V_FLAIR, V_PET and V_PET/FLAIR). These results were compared with molecular genetic testing and 2-year overall survival.

RESULTS

We examined 225 patients with gliomas (97 glioblastomas, 70 astrocytomas, 58 oligodendrogliomas). Accumulation index and volume of 11C-MET in glioblastomas were significantly higher in the general group (AI=2.90, Se 69%, Sp 76%, AUC 0.76; V_PET=24.3 cm3, Se 67%, Sp 60%, AUC 0.65; V_PET/FLAIR 0.46, Se 60%, Sp 69%, AUC 0.67) and within the group of astrocytomas (AI=2.93, Se 68%, Sp 89%, AUC 0.84; V_PET=8.06 cm3, Se 91%, Sp 35%, AUC 0.66; V_PET/FLAIR 0.27, Se 77%, Sp 60%, AUC 0.71). The median 2-year overall survival in patients with glioblastomas was 13 months that was significantly lower compared to IDH «+» gliomas (p<0.0001). There was a relationship between high accumulation index of 11C-MET and shorter overall survival in patients with glioblastomas. Significantly higher AI >3.59 (Se 89%, Sp 67%, AUC 0.79) was additionally obtained in subgroup of patients with glioblastomas >50 years (n=34) for EGFR «+» tumors.

CONCLUSION

We found variable 11C-MET metabolism in WHO 2021 gliomas and confirmed significant difference in metabolic activity and volume of 11C-MET accumulation in glioblastomas compared to IDH «+» gliomas. Moreover, we revealed the relationship between high accumulation index and shorter survival. Analysis of 11C-MET metabolism in patients over 50 years old revealed higher accumulation index in the EGFR «+» group. Further comparison of these imaging methods and assessment of other significant mutations are necessary to identify the anatomical and metabolic patterns of IDH «+» gliomas.

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

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.

Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study

Purpose

We aimed to investigate the predictive models based on O-[2-(¹⁸F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (¹⁸F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas.

Methods

Fifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment ¹⁸F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUV_SD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models.

Results

The AUC of the simple predictive model consists of semi-quantitative parameter SUV_SD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three ¹⁸F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined ¹⁸F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973).

Conclusion

The predictive model combining ¹⁸F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment.

State of Radiomics in Glioblastoma

Radiomics is an emerging discipline that aims to make intelligent predictions and derive medical insights based on quantitative features extracted from medical images as a means to improve clinical diagnosis or outcome. Pertaining to glioblastoma, radiomics has provided powerful, noninvasive tools for gaining insights into pathogenesis and therapeutic responses. Radiomic studies have yielded meaningful biological understandings of imaging features that are often taken for granted in clinical medicine, including contrast enhancement on glioblastoma magnetic resonance imaging, the distance of a tumor from the subventricular zone, and the extent of mass effect. They have also laid the groundwork for noninvasive detection of mutations and epigenetic events that influence clinical outcomes such as isocitrate dehydrogenase (IDH) and O6-methylguanine-DNA methyltransferase (MGMT). In this article, we review advances in the field of glioblastoma radiomics as they pertain to prediction of IDH mutation status and MGMT promoter methylation status, as well as the development of novel, higher order radiomic parameters.

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

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.