TY - JOUR A1 - Werner, Rudolf A. A1 - Ordonez, Alvaro A. A1 - Sanchez-Bautista, Julian A1 - Marcus, Charles A1 - Lapa, Constantin A1 - Rowe, Steven P. A1 - Pomper, Martin G. A1 - Leal, Jeffrey P. A1 - Lodge, Martin A. A1 - Javadi, Mehrbod S. A1 - Jain, Sanjay K. A1 - Higuchi, Takahiro T1 - Novel functional renal PET imaging with 18F-FDS in human subjects JF - Clinical Nuclear Medicine N2 - The novel PET probe 2-deoxy-2-18F-fluoro-D-sorbitol (18F-FDS) has demonstrated favorable renal kinetics in animals. We aimed to elucidate its imaging properties in two human volunteers. 18F-FDS was produced by a simple one-step reduction from 18F-FDG. On dynamic renal PET, the cortex was delineated and activity gradually transited in the parenchyma, followed by radiotracer excretion. No adverse effects were reported. Given the higher spatiotemporal resolution of PET relative to conventional scintigraphy, 18F-FDS PET offers a more thorough evaluation of human renal kinetics. Due to its simple production from 18F-FDG, 18F-FDS is virtually available at any PET facility with radiochemistry infrastructure. KW - 2-deoxy-2-18F-fluoro-D-sorbitol KW - Positronen-Emissions-Tomografie KW - 18F-FDS KW - renal imaging KW - Positron-Emission Tomography KW - split renal function KW - kidney Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-174634 SN - 0363-9762 VL - 44 IS - 5 ER - TY - JOUR A1 - Kazuhino, Koshino A1 - Werner, Rudolf A. A1 - Toriumi, Fuijo A1 - Javadi, Mehrbod S. A1 - Pomper, Martin G. A1 - Solnes, Lilja B. A1 - Verde, Franco A1 - Higuchi, Takahiro A1 - Rowe, Steven P. T1 - Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images JF - Tomography N2 - 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. KW - AI KW - Magnetresonanztomografie KW - artificial intelligence KW - magnetic resonance imaging KW - MRI KW - DCGAN KW - GAN KW - stroke KW - machine learning Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-172185 VL - 4 IS - 4 ER - TY - JOUR A1 - Werner, Rudolf A1 - Hänscheid, Heribert A1 - Leal, Jeffrey P. A1 - Javadi, Mehrbod S. A1 - Higuchi, Takahiro A1 - Lodge, Martin A. A1 - Buck, Andreas K. A1 - Pomper, Martin G. A1 - Lapa, Constantin A1 - Rowe, Steven P. T1 - Impact of Tumor Burden on Quantitative [\(^{68}\)Ga]DOTATOC Biodistribution JF - Molecular Imaging and Biology N2 - Purpose: As has been previously reported, the somatostatin receptor (SSTR) imaging agent [\(^{68}\)Ga]-labeled 1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid-d-Phe(1)-Tyr(3)-octreotate ([\(^{68}\)Ga]DOTATATE) demonstrates lower uptake in normal organs in patients with a high neuroendocrine tumor (NET) burden. Given the higher SSTR affinity of [\(^{68}\)Ga]DOTATATE, we aimed to quantitatively investigate the biodistribution of [\(^{68}\)Ga]-labeled 1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid-d-Phe(1)-Tyr(3)-octreotide ([68Ga]DOTATOC) to determine a potential correlation between uptake in normal organs and NET burden. Procedures: Of the 44 included patients, 36/44 (82%) patients demonstrated suspicious radiotracer uptake on [\(^{68}\)Ga]DOTATOC positron emission tomography (PET)/x-ray computed tomography (CT). Volumes of Interest (VOIs) were defined for tumor lesions and normal organs (spleen, liver, kidneys, adrenals). Mean body weight corrected standardized uptake value (SUV\(_{mean}\)) for normal organs was assessed and was used to calculate the corresponding mean specific activity uptake (Upt: fraction of injected activity per kg of tissue). For the entire tumor burden, SUV\(_{mean}\), maximum standardized uptake value (SUV\(_{max}\)), and the total mass (TBM) was calculated and the decay corrected tumor fractional uptake (TBU) was assessed. A Spearman’s rank correlation coefficient was used to determine the correlations between normal organ uptake and tumor burden. Results: The median SUV\(_{mean}\) was 18.7 for the spleen (kidneys, 9.2; adrenals, 6.8; liver, 5.6). For tumor burden, the median values were SUV\(_{mean}\) 6.9, SUV\(_{max}\) 35.5, TBM 42.6g, and TBU 1.2%. With increasing volume of distribution, represented by lean body mass and body surface area (BSA), Upt decreased in kidneys, liver, and adrenal glands and SUV\(_{mean}\) increased in the spleen. Correlation improved only for both kidneys and adrenals when the influence of the tumor uptake on the activity available for organ uptake was taken into account by the factor 1/(1-TBU). TBU was neither predictive for SUV\(_{mean}\) nor for Upt in any of the organs. The distribution of organ Upt vs. BSA/(1-TBU) were not different for patients with minor TBU (<3%) vs. higher TBU (>7%), indicating that the correlations observed in the present study are explainable by the body size effect. High tumor mass and uptake mitigated against G1 NET. Conclusions: There is no significant impact on normal organ biodistribution with increasing tumor burden on [\(^{68}\)Ga]DOTATOC PET/CT. Potential implications include increased normal organ dose with [\(^{177}\)Lu-DOTA]\(^0\)-D-Phe\(^1\)-Tyr\(^3\)-Octreotide and decreased absolute lesion detection with [\(^{68}\)Ga]DOTATOC in high NET burden. KW - somatostatin receptor KW - Positronen-Emissions-Tomografie KW - quantification KW - [68Ga]DOTATOC KW - neuroendocrine tumor KW - SSTR-PET KW - theranostics Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-170280 ER - TY - JOUR A1 - Werner, Rudolf A. A1 - Bundschuh, Ralph A. A1 - Higuchi, Takahiro A1 - Javadi, Mehrbod S. A1 - Rowe, Steven P. A1 - Zsótér, Norbert A1 - Kroiss, Matthias A1 - Fassnacht, Martin A1 - Buck, Andreas K. A1 - Kreissl, Michael C. A1 - Lapa, Constantin T1 - Volumetric and Texture Analysis of Pretherapeutic \(^{18}\)F-FDG PET can Predict Overall Survival in Medullary Thyroid Cancer Patients Treated with Vandetanib JF - Endocrine N2 - 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. KW - personalized medicine KW - Positronen-Emissions-Tomografie KW - medullary thyroid carcinoma KW - tyrosine kinase inhibitor KW - TKI KW - vandetanib KW - 18F-FDG KW - positron emission tomography KW - 2-deoxy-2-(18F)fluoro-D-glucose KW - PET Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-167910 SN - 1355-008X ER - TY - JOUR A1 - Werner, Rudolf A. A1 - Bundschuh, Ralph A. A1 - Bundschuh, Lena A1 - Javadi, Mehrbod S. A1 - Higuchi, Takahiro A1 - Weich, Alexander A1 - Sheikhbahaei, Sara A1 - Pienta, Kenneth J. A1 - Buck, Andreas K. A1 - Pomper, Martin G. A1 - Gorin, Michael A. A1 - Lapa, Constantin A1 - Rowe, Steven P. T1 - MI-RADS: Molecular Imaging Reporting and Data Systems – A Generalizable Framework for Targeted Radiotracers with Theranostic Implications JF - Annals of Nuclear Medicine N2 - 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. KW - PET KW - Positronen-Emissions-Tomografie KW - prostate cancer KW - neuroendocrine tumor KW - prostate-specific membrane antigen (PSMA) KW - somatostatin receptor (SSTR) KW - positron emission tomography KW - theranostics KW - standardization KW - RADS KW - reporting and data systems KW - personalized medicine Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-166995 SN - 0914-7187 ER -