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  - 
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  -