Refine
Has Fulltext
- yes (15) (remove)
Is part of the Bibliography
- yes (15)
Document Type
- Journal article (9)
- Conference Proceeding (3)
- Preprint (3)
Language
- English (15) (remove)
Keywords
- Positronen-Emissions-Tomografie (12)
- PET (7)
- positron emission tomography (5)
- neuroendocrine tumor (4)
- RADS (3)
- SSTR (3)
- ageing (3)
- prostate cancer (3)
- 11C-HED (2)
- DaTscan (2)
- Ioflupane (2)
- PET/CT (2)
- PSMA (2)
- PSMA-RADS (2)
- Parkinson (2)
- Parkinson Disease (2)
- Parkinson-Krankheit (2)
- SPECT (2)
- [177Lu]-DOTATATE/-DOTATOC (2)
- [68Ga] (2)
- molecular imaging (2)
- personalized medicine (2)
- somatostatin receptor (2)
- theranostics (2)
- tumor heterogeneity (2)
- 11C-Hydroxyephedrine (1)
- 18F-DCFPyL (1)
- 18F-FDG (1)
- 18F-FDS (1)
- 18F-LMI1195 (1)
- 2-deoxy-2-(18F)fluoro-D-glucose (1)
- 2-deoxy-2-18F-fluoro-D-sorbitol (1)
- 68Ga-DOTANOC (1)
- 68Ga-DOTATATE (1)
- 68Ga-DOTATOC (1)
- AI (1)
- DCGAN (1)
- FV45 (1)
- GAN (1)
- MI-RADS (1)
- MRI (1)
- Magnetresonanztomografie (1)
- PROMISE (1)
- PRRT (1)
- Pancreas (1)
- Positron-Emission Tomography (1)
- SSTR-RADS (1)
- TKI (1)
- angiotensin II type 1 receptor (1)
- artificial intelligence (1)
- cardiac sympathetic nervous system (1)
- heart failure (1)
- hydroxyephedrine (1)
- interobserver (1)
- interreader (1)
- kidney (1)
- machine learning (1)
- magnetic resonance imaging (1)
- medullary thyroid carcinoma (1)
- moycardial sympathetic innervation (1)
- myocardial sympathetic innervation imaging (1)
- neuroendocrine neoplasia (1)
- pancreas (1)
- peptide receptor radionuclide therapy (1)
- phaeochromocytoma (1)
- prostate-specific membrane antigen (1)
- prostate-specific membrane antigen (PSMA) (1)
- renal imaging (1)
- renin-angiotensin system (1)
- reporting and data system (1)
- reporting and data systems (1)
- somatostatin receptor (SSTR) (1)
- split renal function (1)
- standardization (1)
- standardized reporting (1)
- storage vesicle turnover (1)
- stroke (1)
- sympathetic nervous system (1)
- tyrosine kinase inhibitor (1)
- valsartan (1)
- vandetanib (1)
Institute
Sonstige beteiligte Institutionen
- Johns Hopkins School of Medicine (15) (remove)
EU-Project number / Contract (GA) number
- 701983 (15)
Both prostate-specific membrane antigen (PSMA)- and somatostatin receptor (SSTR)-targeted positron emission tomography (PET) imaging agents for staging and restaging of prostate carcinoma or neuroendocrine tumors, respectively, are seeing rapidly expanding use. In addition to diagnostic applications, both classes of radiotracers can be used to triage patients for theranostic endoradiotherapy. While interpreting PSMA- or SSTR-targeted PET/computed tomography (CT) scans, the reader has to be aware of certain pitfalls. Adding to the complexity of the interpretation of those imaging agents, both normal biodistribution, and also false-positive and -negative findings differ between PSMA- and SSTR-targeted PET radiotracers. Herein summarized under the umbrella term molecular imaging reporting and data systems (MI-RADS), two novel RADS classifications for PSMA- and SSTR-targeted PET imaging are described (PSMA- and SSTR-RADS). Both framework systems may contribute to increase the level of a reader’s confidence and to navigate the imaging interpreter through indeterminate lesions, so that appropriate workup for equivocal findings can be pursued. Notably, PSMA- and SSTR-RADS are structured in a reciprocal fashion, i.e. if the reader is familiar with one system, the other system can readily be applied as well. In the present review we will discuss the most common pitfalls on PSMA- and SSTR-targeted PET/CT, briefly introduce PSMA- and SSTR-RADS, and define a future role of the umbrella framework MI-RADS compared to other harmonization systems.
Purpose: The metabolically most active lesion in 2-deoxy-2-(\(^{18}\)F)fluoro-D-glucose (\(^{18}\)F-FDG) PET/CT can predict progression-free survival (PFS) in patients with medullary thyroid carcinoma (MTC) starting treatment with the tyrosine kinase inhibitor (TKI) vandetanib. However, this metric failed in overall survival (OS) prediction. In the present proof of concept study, we aimed to explore the prognostic value of intratumoral textural features (TF) as well as volumetric parameters (total lesion glycolysis, TLG) derived by pre-therapeutic \(^{18}\)F-FDG PET.
Methods: Eighteen patients with progressive MTC underwent baseline \(^{18}\)F-FDG PET/CT prior to and 3 months after vandetanib initiation. By manual segmentation of the tumor burden at baseline and follow-up PET, intratumoral TF and TLG were computed. The ability of TLG, imaging-based TF, and clinical parameters (including age, tumor marker doubling times, prior therapies and RET (rearranged during transfection) mutational status) for prediction of both PFS and OS were evaluated.
Results: The TF Complexity and the volumetric parameter TLG obtained at baseline prior to TKI initiation successfully differentiated between low- and high-risk patients. Complexity allocated 10/18 patients to the high-risk group with an OS of 3.3y (vs. low-risk group, OS=5.3y, 8/18, AUC=0.78, P=0.03). Baseline TLG designated 11/18 patients to the high-risk group (OS=3.5y vs. low-risk group, OS=5y, 7/18, AUC=0.83, P=0.005). The Hazard Ratio for cancer-related death was 6.1 for Complexity (TLG, 9.5). Among investigated clinical parameters, the age at initiation of TKI treatment reached significance for PFS prediction (P=0.02, OS, n.s.).
Conclusions: The TF Complexity and the volumetric parameter TLG are both independent parameters for OS prediction.
More than 25 years after the first peptide receptor radionuclide therapy (PRRT), the concept of somatostatin receptor (SSTR)-directed imaging and therapy for neuroendocrine tumors (NET) is seeing rapidly increasing use. To maximize the full potential of its theranostic promise, efforts in recent years have expanded recommendations in current guidelines and included the evaluation of novel theranostic radiotracers for imaging and treatment of NET. Moreover, the introduction of standardized reporting framework systems may harmonize PET reading, address pitfalls in interpreting SSTR-PET/CT scans and guide the treating physician in selecting PRRT candidates. Notably, the concept of PRRT has also been applied beyond oncology, e.g. for treatment of inflammatory conditions like sarcoidosis. Future perspectives may include the efficacy evaluation of PRRT compared to other common treatment options for NET, novel strategies for closer monitoring of potential side effects, the introduction of novel radiotracers with beneficial pharmacodynamic and kinetic properties or the use of supervised machine learning approaches for outcome prediction. This article reviews how the SSTR-directed theranostic concept is currently applied and also reflects on recent developments that hold promise for the future of theranostics in this context.
Background: \(^{18}\)F-N-[3-bromo-4-(3-fluoro-propoxy)-benzyl]-guanidine (\(^{18}\)F-LMI1195) is a new class of PET tracer designed for sympathetic nervous imaging of the heart. The favorable image quality with high and specific neural uptake has been previously demonstrated in animals and humans, but intracellular behavior is not yet fully understood. The aim of the present study is to verify whether it is taken up in storage vesicles and released in company with vesicle turnover.
Results: Both vesicle-rich (PC12) and vesicle-poor (SK-N-SH) norepinephrine-expressing cell lines were used for in vitro tracer uptake studies. After 2 h of \(^{18}\)F-LMI1195 preloading into both cell lines, effects of stimulants for storage vesicle turnover (high concentration KCl (100 mM) or reserpine treatment) were measured at 10, 20, and 30 min. \(^{131}\)I-meta-iodobenzylguanidine (\(^{131}\)I-MIBG) served as a reference. Both high concentration KCl and reserpine enhanced \(^{18}\)F-LMI1195 washout from PC12 cells, while tracer retention remained stable in the SK-N-SH cells. After 30 min of treatment, 18F-LMI1195 releasing index (percentage of tracer released from cells) from vesicle-rich PC12 cells achieved significant differences compared to cells without treatment condition. In contrast, such effect could not be observed using vesicle-poor SK-N-SH cell lines. Similar tracer kinetics after KCl or reserpine treatment were also observed using 131I-MIBG. In case of KCl exposure, Ca\(^{2+}\)-free buffer with the calcium chelator, ethylenediaminetetracetic acid (EDTA), could suppress the tracer washout from PC12 cells. This finding is consistent with the tracer release being mediated by Ca\(^{2+}\) influx resulting from membrane depolarization.
Conclusions: Analogous to \(^{131}\)I-MIBG, the current in vitro tracer uptake study confirmed that \(^{131}\)F-LMI1195 is also stored in vesicles in PC12 cells and released along with vesicle turnover. Understanding the basic kinetics of \(^{18}\)FLMI1195 at a subcellular level is important for the design of clinical imaging protocols and imaging interpretation.
Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)-based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%-60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for "data-hungry" technologies, such as supervised machine learning approaches, in various clinical applications.