Amino Acid PET Imaging of Glioma

Radiosynthesis and Preclinical Evaluation of m-[18F]FET and [18F]FET-OMe as Novel [18F]FET Analogs for Brain Tumor Imaging

O-([18F]Fluoroethyl)-l-tyrosine ([18F]FET) is actively transported into the brain and cancer cells by LAT1 and possibly other amino acid transporters, which enables brain tumor imaging by positron emission tomography (PET). However, tumor delivery of this probe in the presence of competing amino acids may be limited by a relatively low affinity for LAT1. The aim of the present work was to evaluate the meta-substituted [18F]FET analog m-[18F]FET and the methyl ester [18F]FET-OMe, which were designed to improve tumor delivery by altering the physicochemical, pharmacokinetic, and/or transport properties. Both tracers could be prepared with good radiochemical yields of 41-56% within 66-90 min. Preclinical evaluation with [18F]FET as a reference tracer demonstrated reduced in vitro uptake of [18F]FET-OMe by U87 glioblastoma cells and no advantage for in vivo tumor imaging. In contrast, m-[18F]FET showed significantly improved in vitro uptake and accelerated in vivo tumor accumulation in an orthotopic glioblastoma model. As such, our work identifies m-[18F]FET as a promising alternative to [18F]FET for brain tumor imaging that deserves further evaluation with regard to its transport properties and in vivo biodistribution.

Evaluation of 3-l- and 3-d-[18F]Fluorophenylalanines as PET Tracers for Tumor Imaging

Simple Summary

The early detection and treatment of malignant brain tumors can significantly improve the survival time and life quality of affected patients. Whereas positron emission tomography (PET) with O-(2-[¹⁸F]fluoroethyl)tyrosine ([¹⁸F]FET) offers improved diagnostic accuracy compared to other imaging methods, there is still a need for PET tracers with better tumor-specificity. A higher protein incorporation rate, as well as a higher affinity for the amino acid transporter LAT1, could provide probes with superior image quality compared to [¹⁸F]FET. The aim of the present study was a preclinical evaluation of the two enantiomeric phenylalanine (Phe) analogues, 3-l- and 3-d-[¹⁸F]fluorophenylalanine ([¹⁸F]FPhes), as possible alternatives to [¹⁸F]FET. Based on promising in vitro evaluation results, the radiolabeled amino acids were studied in vivo in two subcutaneous and one orthotopic rodent tumor xenograft models using µPET. The results show that 3-l- and 3-d-[¹⁸F]FPhe enable high-quality visualization of tumors with certain advantages over [¹⁸F]FET, making them promising candidates for further preclinical and clinical evaluations.

Abstract

Purpose: The preclinical evaluation of 3-l- and 3-d-[¹⁸F]FPhe in comparison to [¹⁸F]FET, an established tracer for tumor imaging. Methods: In vitro studies were conducted with MCF-7, PC-3, and U87 MG human tumor cell lines. In vivo µPET studies were conducted in healthy rats with/without the inhibition of peripheral aromatic l-amino acid decarboxylase by benserazide pretreatment (n = 3 each), in mice bearing subcutaneous MCF-7 or PC-3 tumor xenografts (n = 10), and in rats bearing orthotopic U87 MG tumor xenografts (n = 14). Tracer accumulation was quantified by SUV_max, SUV_mean and tumor-to-brain ratios (TBrR). Results: The uptake of 3-l-[¹⁸F]FPhe in MCF-7 and PC-3 cells was significantly higher relative to [¹⁸F]FET. The uptake of all three tracers was significantly reduced by the suppression of amino acid transport systems L or ASC. 3-l-[¹⁸F]FPhe but not 3-d-[¹⁸F]FPhe exhibited protein incorporation. In benserazide-treated healthy rats, brain uptake after 42–120 min was significantly higher for 3-d-[¹⁸F]FPhe vs. 3-l-[¹⁸F]FPhe. [¹⁸F]FET showed significantly higher uptake into subcutaneous MCF-7 tumors (52–60 min p.i.), while early uptake into orthotopic U87 MG tumors was significantly higher for 3-l-[¹⁸F]FPhe (SUV_max: 3-l-[¹⁸F]FPhe, 107.6 ± 11.3; 3-d-[¹⁸F]FPhe, 86.0 ± 4.3; [¹⁸F]FET, 90.2 ± 7.7). Increased tumoral expression of LAT1 and ASCT2 was confirmed immunohistologically. Conclusion: Both novel tracers enable accurate tumor delineation with an imaging quality comparable to [¹⁸F]FET.

Machine Learning Model to Predict Pseudoprogression Versus Progression in Glioblastoma Using MRI: A Multi-Institutional Study (KROG 18-07)

Simple Summary

Even after the introduction of a standard regimen consisting of concurrent chemoradiotherapy and adjuvant temozolomide, patients with glioblastoma multiforme mostly experience disease progression. Clinicians often encounter a situation where they need to distinguish progressive disease from pseudoprogression after treatment. We tried to investigate the feasibility of machine learning algorithm to distinguish pseudoprogression from progressive disease. In multi-institutional dataset, the developed machine learning model showed an acceptable performance. This algorithm involving MRI data and clinical features could help making decision during patients’ disease course. For the practical use, we calibrated the machine learning model to offer the probability of pseudoprogression to clinicians, then we constructed the web-based user interface to access the model.

Abstract

Some patients with glioblastoma show a worsening presentation in imaging after concurrent chemoradiation, even when they receive gross total resection. Previously, we showed the feasibility of a machine learning model to predict pseudoprogression (PsPD) versus progressive disease (PD) in glioblastoma patients. The previous model was based on the dataset from two institutions (termed as the Seoul National University Hospital (SNUH) dataset, N = 78). To test this model in a larger dataset, we collected cases from multiple institutions that raised the problem of PsPD vs. PD diagnosis in clinics (Korean Radiation Oncology Group (KROG) dataset, N = 104). The dataset was composed of brain MR images and clinical information. We tested the previous model in the KROG dataset; however, that model showed limited performance. After hyperparameter optimization, we developed a deep learning model based on the whole dataset (N = 182). The 10-fold cross validation revealed that the micro-average area under the precision-recall curve (AUPRC) was 0.86. The calibration model was constructed to estimate the interpretable probability directly from the model output. After calibration, the final model offers clinical probability in a web-user interface.

Response Assessment in Neuro-Oncology Criteria for Gliomas: Practical Approach Using Conventional and Advanced Techniques

The Response Assessment in Neuro-Oncology criteria were developed as an objective tool for radiologic assessment of treatment response in high-grade gliomas. Imaging plays a critical role in the management of the patient with glioma, from initial diagnosis to posttreatment follow-up, which can be particularly challenging for radiologists. Interpreting findings after surgery, radiation, and chemotherapy requires profound knowledge about the tumor biology, as well as the peculiar changes expected to ensue as a consequence of each treatment technique. In this article, we discuss the imaging findings associated with tumor progression, tumor response, pseudoprogression, and pseudoresponse according to the Response Assessment in Neuro-Oncology criteria for high-grade and lower-grade gliomas. We describe relevant practical issues when evaluating patients with glioma, such as the need for imaging in the first 48 hours, the radiation therapy planning and isodose curves, the significance of T2/FLAIR hyperintense lesions, the impact of the timing for the evaluation after radiation therapy, and the definition of progressive disease on the histologic specimen. We also illustrate the correlation among the findings on conventional MR imaging with advanced techniques, such as perfusion, diffusion-weighted imaging, spectroscopy, and amino acid PET. Because many of the new lesions represent a mixture of tumor cells and tissue with radiation injury, the radiologist aims to identify the predominant component of the lesion and categorize the findings according to Response Assessment in Neuro-Oncology criteria so that the patient can receive the best treatment.