PET/MR in recurrent glioblastoma patients treated with regorafenib: [18F]FET and DWI-ADC for response assessment and survival prediction

Lombardi, Giuseppe; Spimpolo, Alessandro; Berti, Sara; Campi, Cristina; Anglani, Mariagiulia; Simeone, Rossella; Evangelista, Laura; Causin, Francesco; Zorzi, Giovanni; Gorgoni, Giancarlo; Caccese, Mario; Padovan, Marta; Zagonel, Vittorina; Cecchin, Diego

doi:10.1259/bjr.20211018

Cited by 16 publications

(10 citation statements)

References 46 publications

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“…MR images have been used more frequently to predict survival in a radiomics-based approach. The performance reported in our study based on 18 F-FET PET images are in line with previously reported outcomes on MRI-based radiomics in glioblastoma [ 33 – 38 ]. These studies include also more recent extensions from traditional radiomics to deep learning models, designed to include traditional risk factors as potential predictors [ 35 ].…”

Section: Discussionsupporting

confidence: 91%

“…Therefore, 18 F-FET PET images are suggested as valuable candidates for the prediction of overall survival in primary glioblastoma patients. The complimentary use of MRI and 18 F-FET PET images has been recently shown in recurrent glioblastoma patients to derive independent biomarkers of response to treatment [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…As further limitation, no comparison with MRI-based radiomic features has been carried out in this work. Different studies have investigated the use of quantitative features from MRI and 18 F-FET PET for treatment response and survival [ 6 , 10 , 38 ], highlighting the complimentary role of these imaging modalities. A comparative radiomics-based analysis is therefore suggested to highlight the need of dedicated imaging protocols for glioblastoma survival prediction.…”

Section: Discussionmentioning

confidence: 99%

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18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

et al. 2022

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Background Quantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy. Methods A dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results. Results First order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression. Conclusions 18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

et al. 2022

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“…Again, regorafenib proved to have an effectiveness comparable to that of REGOMA, with an even longer survival time [ 10 ]. Although clinical data are accumulating, demonstrating encouraging efficacy and tolerability of regorafenib in GBM patients [ 10 , 11 , 15 , 46 ], the mechanisms governing GBM response to regorafenib have been poorly investigated. Santangelo and colleagues [ 12 ] identified five biomarkers which could help in identifying a subpopulation of GBM patients exhibiting a striking survival advantage when treated with regorafenib.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Glioblastoma Cells Response to Regorafenib

Mongiardi

Buccarelli

Formato

et al. 2022

Cancers

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Glioblastoma (GBM), the most malignant primary brain tumor in adults. Although not frequent, it has a relevant social impact because the peak incidence coincides with the age of professional maturity. A number of novel treatments have been proposed, yet clinical trials have been disappointing. Recently, a phase II clinical trial (REGOMA) demonstrated that the multikinase inhibitor regorafenib significantly increased the median overall survival (OS) of GBM patients when compared to lomustine-treated patients. On this basis, the National Comprehensive Cancer Network (NCCN) 2020 Guidelines included regorafenib as a preferred regimen in relapsed GBM treatment. Despite the use in GBM patients’ therapy, little is known about the molecular mechanisms governing regorafenib effectiveness on the GBM tumor. Here we report an in vitro characterization of GBM tumor cells’ response to regorafenib, performed both on cell lines and on patient-derived glioma stem cells (GSCs). Overall, regorafenib significantly reduced cell growth of 2D tumor cell cultures and of 3D tumor spheroids. Strikingly, this effect was accompanied by transcriptional regulation of epithelial to mesenchymal transition (EMT) genes and by an increased ability of surviving tumor cells to invade the surrounding matrix. Taken together, our data suggest that regorafenib limits cell growth, however, it might induce an invasive phenotype.

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“…In patients with recurrent HGGs treated with regorafenib (multikinase inhibitor), FET and DWI-ADC metrics can predict the overall survival, and could serve as semiquantitative independent biomarkers of response to treatment. 46 A recent study analyzed data from patients with GBM who received autologous dendritic cell vaccination therapy. In MR imaging for suspicion for GBM recurrence, FET-PET showed congruent tumor progression in 3/5 patients and TRC in the remaining 2/5.…”

Section: Monitoring Treatment Response To Newer Drugs In Recurrent Hg...mentioning

confidence: 99%

Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know

Soni¹,

Ora²,

Jena³

et al. 2023

AJNR Am J Neuroradiol

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PET with amino acid tracers provides additional insight beyond MR imaging into the biology of gliomas that can be used for initial diagnosis, delineation of tumor margins, planning of surgical and radiation therapy, assessment of residual tumor, and evaluation of posttreatment response. Hybrid PET MR imaging allows the simultaneous acquisition of various PET and MR imaging parameters in a single investigation with reduced scanning time and improved anatomic localization. This review aimed to provide neuroradiologists with a concise overview of the various amino acid tracers and a practical understanding of the clinical applications of amino acid PET MR imaging in glioma management. Future perspectives in newer advances, novel radiotracers, radiomics, and cost-effectiveness are also outlined.ABBREVIATIONS: AAT ¼ amino acid tracer; AA ¼ amino acid; ASCT ¼ alanine-serine-cysteine transporter; BTV ¼ biologic tumor volume; GBM ¼ glioblas-G liomas represent approximately 80% of malignant brain tumors, with an annual incidence rate of 5.6 cases per 100,000 individuals worldwide. Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor, representing approximately 50% of all gliomas and 16% of all brain tumors. 1 Depending on the size and extent of these tumors, the standard of care for newly diagnosed GBMs usually includes maximal surgical resection followed by radiation and chemotherapy. Despite substantial development in managing high-grade gliomas (HGGs), the median survival is ,15 months, with 1-and 5-year survival rates of 40% and 5.5%, respectively. 2 Imaging is crucial for diagnosing, guiding biopsy, surgical planning, and distinguishing treatment-related changes (TRC) from recurrence in glioma management. MR imaging is the primary imaging technique; however, it lacks specificity to distinguish between viable neoplastic tissue and tumor-free areas.Advanced MR imaging techniques such as PWI, DWI, DTI, MRS, and molecular imaging (PET) facilitate visualization and quantification of different metabolic processes and improve overall diagnostic performance in brain tumors. Hybrid PET MR imaging with novel radiotracers provides a noninvasive, simultaneous assessment of brain tumor morphologic, functional, metabolic, and molecular parameters. 3,4 This review aimed to provide neuroradiologists with a concise overview of the various amino acid tracers (AATs) and a practical understanding of the clinical applications of amino acid (AA) PET MR imaging in glioma management. We will review the current literature regarding AA-PET MR imaging in glioma treatment and discuss its role in the initial diagnosis, delineation of tumor margin, planning of radiation therapy, assessment of residual tumor, and evaluation of treatment response. We will sum up with future perspectives on newer advances, novel radiotracers, radiomics, and cost-effectiveness. RadiopharmaceuticalsThe Joint European Association of Nuclear Medicine (EANM)/ European Association of Neuro-Oncology (EANO)/Response Assessment in Neuro-Oncology (RA...

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PET/MR in recurrent glioblastoma patients treated with regorafenib: [18F]FET and DWI-ADC for response assessment and survival prediction

Cited by 16 publications

References 46 publications

18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

Characterization of Glioblastoma Cells Response to Regorafenib

Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know

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