Purpose: In addition to rodent models, the chick embryo model has gained attention for radiotracer evaluation. Previous studies have investigated tumours on the chorioallantoic membrane (CAM), but its value for radiotracer imaging of intracerebral tumours has yet to be demonstrated.
Procedures: Human U87 glioblastoma cells and U87-IDH1 mutant glioma cells were implanted into the brains of chick embryos at developmental day 5.
One of the most common clinical indications for amino acid PET using the tracer -(2-[F]-fluoroethyl)-l-tyrosine (F-FET) is the differentiation of tumor relapse from treatment-related changes in patients with gliomas. A subset of patients may present with an uptake of F-FET close to recommended threshold values. The goal of this study was to investigate the frequency of borderline cases and the role of quantitative F-FET PET parameters in this situation.
View Article and Find Full Text PDFPurpose: Especially in Europe, amino acid PET is increasingly integrated into multidisciplinary neuro-oncological tumor boards (MNTBs) to overcome diagnostic uncertainties such as treatment-related changes. We evaluated the accuracy of MNTB decisions that included the O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET information compared with FET PET results alone to differentiate tumor relapse from treatment-related changes.
Patients And Methods: In a single academic center, we retrospectively evaluated 180 MNTB decisions of 151 patients with CNS WHO grade 3 or 4 gliomas (n = 122) or brain metastases (n = 29) presenting equivocal MRI findings following anticancer treatment.
The phase-3 INDIGO trial demonstrated that the isocitrate dehydrogenase () inhibitor vorasidenib significantly prolonged progression-free survival and delayed intervention in patients with CNS WHO grade 2 gliomas. However, conventional MRI showed limited response, with only 11% of patients having objective responses. Studies suggest that serial PET imaging with radiolabeled amino acids, such as -(2-[ F]-fluoroethyl)-L-tyrosine (FET) PET, may provide earlier and more informative assessments of treatment response than MRI.
View Article and Find Full Text PDFThe development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. Furthermore, promising future directions, including the use of AI for automated response assessment in neuro-oncology, are discussed.
View Article and Find Full Text PDFObjective: The treatment with Lutetium PSMA (Lu-PSMA) in patients with metastatic castration-resistant prostate cancer (mCRPC) has recently been approved by the FDA and EMA. Since treatment success is highly variable between patients, the prediction of treatment response and identification of short- and long-term survivors after treatment could help tailor mCRPC diagnosis and treatment accordingly. The aim of this study is to investigate the value of radiomic parameters extracted from pretreatment Ga-PSMA PET images for the prediction of treatment response.
View Article and Find Full Text PDFObjectives: Achieving a consensus on a definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, to assess the perspective of experts on important challenges for a successful clinical workflow implementation.
Materials And Methods: The consensus was achieved by a multi-stage process.
PET using the radiolabeled amino acid -(2-[F]fluoroethyl)-l-tyrosine (F-FET) has been shown to be of value for treatment monitoring in patients with brain metastases after multimodal therapy, especially in clinical situations with equivocal MRI findings. As medical procedures must be justified socioeconomically, we determined the effectiveness and cost-effectiveness of F-FET PET for treatment monitoring of multimodal therapy, including checkpoint inhibitors, targeted therapies, radiotherapy, and combinations thereof in patients with brain metastases secondary to melanoma or non-small cell lung cancer. We analyzed already-published clinical data and calculated the associated costs from the German statutory health insurance system perspective.
View Article and Find Full Text PDFObjective: Quantitative values derived from PET brain images are of high interest for neuroscientific applications. Insufficient DT correction (DTC) can lead to a systematic bias of the output parameters obtained by a detailed analysis of the time activity curves (TACs). The DTC method currently used for the Siemens 3T MR BrainPET insert is global, i.
View Article and Find Full Text PDFBackground: Radiological progression may originate from progressive disease (PD) or pseudoprogression/treatment-associated changes. We assessed radiological progression in O6-methylguanine-DNA methyltransferase (MGMT) promoter-methylated glioblastoma treated with standard-of-care chemoradiotherapy with or without the integrin inhibitor cilengitide according to the modified response assessment in neuro-oncology (RANO) criteria of 2017.
Methods: Patients with ≥ 3 follow-up MRIs were included.
Background: In glioma patients, tumor growth and subsequent treatments are associated with various types of brain lesions. We hypothesized that cognitive functioning in these patients critically depends on the maintained structural connectivity of multiple brain networks.
Methods: The study included 121 glioma patients (median age, 52 years; median Eastern Cooperative Oncology Group performance score 1; CNS-WHO Grade 3 or 4) after multimodal therapy.
Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine.
View Article and Find Full Text PDFPurpose: Morphological imaging using MRI is essential for brain tumour diagnostics. Dynamic susceptibility contrast (DSC) perfusion-weighted MRI (PWI), as well as amino acid PET, may provide additional information in ambiguous cases. Since PWI is often unavailable in patients referred for amino acid PET, we explored whether maps of relative cerebral blood volume (rCBV) in brain tumours can be extracted from the early phase of PET using O-(2-F-fluoroethyl)-L-tyrosine (F-FET).
View Article and Find Full Text PDFDigitization in the healthcare sector and the support of clinical workflows with artificial intelligence (AI), including AI-supported image analysis, represent a great challenge and equally a promising perspective for preclinical and clinical nuclear medicine. In Germany, the Medical Informatics Initiative (MII) and the Network University Medicine (NUM) are of central importance for this transformation. This review article outlines these structures and highlights their future role in enabling privacy-preserving federated multi-center analyses with interoperable data structures harmonized between site-specific IT infrastructures.
View Article and Find Full Text PDFEvaluation of metabolic tumor volume (MTV) changes using amino acid PET has become an important tool for response assessment in brain tumor patients. MTV is usually determined by manual or semiautomatic delineation, which is laborious and may be prone to intra- and interobserver variability. The goal of our study was to develop a method for automated MTV segmentation and to evaluate its performance for response assessment in patients with gliomas.
View Article and Find Full Text PDFAdvanced MRI methods and PET using radiolabelled amino acids provide valuable information, in addition to conventional MR imaging, for brain tumour diagnostics. These methods are particularly helpful in challenging situations such as the differentiation of malignant processes from benign lesions, the identification of non-enhancing glioma subregions, the differentiation of tumour progression from treatment-related changes, and the early assessment of responses to anticancer therapy. The debate over which of the methods is preferable in which situation is ongoing, and has been addressed in numerous studies.
View Article and Find Full Text PDFThe translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms.
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