Correlation between dynamic susceptibility contrast perfusion MRI and genomic alterations in glioblastoma

Advances in liquid biopsy and virtual biopsy for care of patients with glioma: a narrative review

INTRODUCTION

The World Health Organization's 2021 classification of central nervous system neoplasms incorporated molecular and genetic features for classifying gliomas. Classification of gliomas located in deep-seated structures became a clinical conundrum given the absence of crucial pathological and molecular data. Advances in noninvasive imaging modalities offered virtual biopsy as a novel solution to this problem by identifying surrogate radiomic signatures. Liquid biopsies of blood or cerebrospinal fluid provided another enormous opportunity for identifying genomic, metabolomic and proteomic signatures.

AREAS COVERED

We summarize and appraise the current state of evidence with regards to virtual biopsy and liquid biopsy in the care of patients with gliomas. PubMed, Embase and Google Scholar were searched on 7/30/2024 for relevant articles published after the year 2013 in the English language.

EXPERT OPINION

A large body of preclinical and preliminary clinical evidence suggests that virtual biopsy is possible with the combined use of multiple novel imaging modalities in conjunction with machine learning and radiomics. Likewise, liquid biopsy in conjunction with focused ultrasound may be a valuable tool to obtain proteomic and genomic data regarding glioma in a minimally invasive manner. These modalities will likely become an integral part of care for patients with glioma in the future.

Deciphering glioblastoma: Unveiling imaging markers for predicting MGMT promoter methylation status

Glioblastoma, the most common primary malignant tumor of the central nervous system in adults, is also among the most lethal. Despite a comprehensive treatment approach which utilizes surgery and postoperative chemoradiation, prognosis typically remains dismal. However certain epigenetic modifications, such as methylation of the MGMT promoter, have been proven to correlate with improved post-treatment outcomes. The 2021 WHO classification emphasizes molecular characteristics, highlighting shared genomic alterations across different grades and positioning MGMT methylation as a key influencer of outcomes. A combined diagnostic approach involving current imaging technology and emerging radiomics and deep learning models may allow for timely and accurate prediction of MGMT methylation status and therefore earlier and more individualized treatment and prognostication. Though these advanced radiomics models are rapidly emerging, additional development, standardization, and implementation may lead to a higher and more individualized level of patient care. This review explores the potential of imaging features in predicting MGMT promoter methylation, a critical determinant of therapeutic response and patient outcomes.

Dynamic susceptibility contrast‑enhanced perfusion magnetic resonance imaging parameters for predicting MGMT promoter methylation and prognostic value in newly diagnosed patients with glioblastoma

O6-methylguanine DNA methyltransferase (MGMT) promoter methylation is an important clinical biomarker of newly diagnosed glioblastoma. Previous radiological studies using dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) perfusion have aimed to predict MGMT methylation status non-invasively in gliomas with radiological characteristics. The possibility of predicting MGMT methylation status using DSC-MRI perfusion with a radiological approach remains controversial. The present study aimed to evaluate the usefulness of MRI perfusion parameters as non-invasive markers to predict MGMT methylation status and prognosis in newly diagnosed glioblastoma patients. Thus, 50 patients with histologically confirmed primary glioblastoma, IDH-wildtype who underwent tumor resection at Osaka University Hospital (Suita, Japan) between January 2017 and January 2023 were included in this study. The mean cerebral blood volume (CBV) ratio (rCBV) and cerebral blood flow (CBF) ratio (rCBF) for tumors with MGMT methylation (mean rCBV:2.09 and mean rCBF:3.08) were significantly higher compared with those for tumors without MGMT methylation (mean rCBV:1.33 and mean rCBF:1.85; P<0.05). While patients with MGMT methylation had longer progression-free survival (PFS) compared with those without MGMT methylation (P<0.05), there was no significant difference in OS with or without MGMT methylation (P=0.06). By contrast, there was no association between MRI perfusion parameters and OS or PFS in patients with glioblastoma. Furthermore, the association between CBV, CBF, MGMT promotor methylation status, OS, and PFS were explored. There was no significant prognostic difference between low vascularity tumors (rCBV <1.3 or rCBF <1.8) with or without MGMT methylation. On the other hand, high vascularity tumors (rCBF ≥1.8) with MGMT promotor methylation were associated to longer OS and PFS compared with those without. However, there was no association between MGMT methylation status and OS or PFS in patients with high rCBV (rCBV ≥1.3). The present study indicated that CBV and CBF could be used to predict the MGMT methylation status in glioblastomas. However, the prognostic value of tumor vascularity and MGMT methylation status may be limited.

Analysis of genetic test results in 378 patients suspected of thalassaemia

OBJECTIVE

To analyze the genetic test results of 378 patients suspected of thalassemia.

METHODS

378 suspected thalassemia patients in Shaoxing People's Hospital from 2014 to 2020 were selected and venous blood was tested using Gap-PCR and PCR-reversed dot blottin. The distribution of genotypes and other information of gene-positive patients was observed.

RESULTS

Thalassemia genes were detected in 222 cases, with an overall detection rate of 58.7%, of which 41.4% were α deletion type, 1.35% were α dot, 52.7% were α thalassemia, and 4.5% were αβ complex type. Among the 86 people with provincial household registration, the α-thalassemia gene accounted for 65.1% and the β-thalassemia gene accounted for 25.6%. Follow-up found that Shaoxing nationality accounted for 53.1% of positive patients, of which β-thalassemia gene accounted for 72.9% and α-thalassemia gene accounted for 25.4%; other cities in the province accounted for 8.1% of the total. Other provinces and cities accounted for 38.7%, most of which were from Guangxi and Guizhou. Among all positive patients, the most common α-thalassemia genotypes were --sea / αα, --α / αα,--α 3.7 4.2 / αα , --α3.7 / --sea. The most common mutations in β-thalassemia were IVS-II-654, CD41-42, CD17 and CD14-15.

CONCLUSION

The thalassemia gene carrier status was sporadically distributed outside the traditional thalassemia high prevalence areas. The local population in Shaoxing has a high detection rate of thalassemia genes, and the genetic composition is different from the traditional high prevalence area of thalassemia in the south.

Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status

Background.Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis.O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients.Purpose.To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities.Methods.Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset.Results.The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively.Conclusions.SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.

Postoperative disappearance of leptomeningeal enhancement around the brainstem in glioblastoma

PURPOSE

Leptomeningeal enhancement (LME) suggests leptomeningeal dissemination (LMD) of tumor cells, which is a complication of end-stage glioblastoma, and is associated with a poor prognosis. However, magnetic resonance imaging (MRI) occasionally indicates the disappearance of peri-brainstem LME after surgical resection of glioblastoma. Since preoperative LMD may affect treatment indications, we aimed to analyze the clinical significance of preoperative LME of the brainstem in glioblastoma.

METHODS

We retrospectively collected clinical and radiological data from consecutive patients with glioblastoma and preoperative LME of the brainstem, who were treated at our hospital between 2017 and 2020.

RESULTS

Among 112 patients with glioblastoma, nine (8%) showed preoperative LME of the brainstem. In comparison with tumors without LME, tumor size was significantly associated with the preoperative LME of the brainstem (p = 0.016). In addition, there was a trend toward significance for a relationship between deep tumor location and preoperative LME of the brainstem (p = 0.058). Notably, among six patients who underwent surgical resection for glioblastoma with LME of the brainstem, four showed significant radiological disappearance of the LME on postoperative MRI. This suggests that the LME did not result from LMD in these cases. Moreover, these four patients lived longer than would be expected from the presence of LMD. However, this LME disappearance was not observed after biopsy or chemoradiotherapy.

CONCLUSIONS

These findings suggest that preoperative LME does not necessarily indicate the presence of untreatable LMD; moreover, LME may disappear after surgical tumor resection. Thus, transient preoperative LME could be attributed to other mechanisms, including impaired venous flow due to intratumoral arteriovenous shunts, which can be resolved by reducing the tumor burden.

Evaluation of isocitrate dehydrogenase mutation in 2021 world health organization classification grade 3 and 4 glioma adult-type diffuse gliomas with 18F-fluoromisonidazole PET

PURPOSE

This study aimed to investigate the uptake characteristics of 18F-fluoromisonidazole (FMISO), in mutant-type isocitrate dehydrogenase (IDH-mutant, grade 3 and 4) and wild-type IDH (IDH-wildtype, grade 4) 2021 WHO classification adult-type diffuse gliomas.

MATERIALS AND METHODS

Patients with grade 3 and 4 adult-type diffuse gliomas (n = 35) were included in this prospective study. After registering 18F-FMISO PET and MR images, standardized uptake value (SUV) and apparent diffusion coefficient (ADC) were evaluated in hyperintense areas on fluid-attenuated inversion recovery (FLAIR) imaging (HIA), and in contrast-enhanced tumors (CET) by manually placing 3D volumes of interest. Relative SUVmax (rSUVmax) and SUVmean (rSUVmean), 10th percentile of ADC (ADC10pct), mean ADC (ADCmean) were measured in HIA and CET, respectively.

RESULTS

rSUVmean in HIA and rSUVmean in CET were significantly higher in IDH-wildtype than in IDH-mutant (P = 0.0496 and 0.03, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET, that of rSUVmax and ADC10pct in CET, that of rSUVmean in HIA and ADCmean in CET, were able to differentiate IDH-mutant from IDH-wildtype (AUC 0.80). When confined to astrocytic tumors except for oligodendroglioma, rSUVmax, rSUVmean in HIA and rSUVmean in CET were higher for IDH-wildtype than for IDH-mutant, but not significantly (P = 0.23, 0.13 and 0.14, respectively). The combination of FMISO rSUVmean in HIA and ADC10pct in CET was able to differentiate IDH-mutant (AUC 0.81).

CONCLUSION

PET using 18F-FMISO and ADC might provide a valuable tool for differentiating between IDH mutation status of 2021 WHO classification grade 3 and 4 adult-type diffuse gliomas.

Combining amide proton transfer-weighted and arterial spin labeling imaging to differentiate solitary brain metastases from glioblastomas

PURPOSE

To evaluate the clinical utility of amide proton transfer-weighted imaging (APTw) and arterial spin labeling (ASL) in differentiating solitary brain metastases (SBMs) from glioblastomas (GBMs).

METHODS

Forty-eight patients diagnosed with brain tumors were enrolled. All patients underwent conventional MRI, APTw, and ASL scans on a 3.0 T MRI system. The mean APTw value and mean cerebral blood flow (CBF) value were measured. The differences in various parameters between GBMs and SBMs were assessed using the independent-samples t-test. The quantitative performance of these MRI parameters in distinguishing between GBMs and SBMs was evaluated using receiver operating characteristic (ROC) curve analysis.

RESULTS

GBMs exhibited significantly higher APTw and CBF values in peritumoral regions compared with SBMs (P < 0.05). There was no significant difference between SBMs and GBMs in tumor cores. APTw MRI had a higher diagnostic efficiency in differentiating SBMs from GBMs (area under the curve [AUC]: 0.864; 75.0% sensitivity and 81.8% specificity). Combined use of APTw and CBF value increased the AUC to 0.927.

CONCLUSION

APTw may be superior to ASL for distinguishing between SBMs and GBMs. Combination of APTw and ASL showed better discrimination and a superior diagnostic performance.

Hemodynamic Imaging in Cerebral Diffuse Glioma-Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.