PT - JOURNAL ARTICLE AU - Johannes Grueneisen AU - Benedikt Schaarschmidt AU - Aydin Demircioglu AU - Michal Chodyla AU - Ole Martin AU - Stefanie Bertram AU - Axel Wetter AU - Sebastian Bauer AU - Wolfgang Peter Fendler AU - Lars Podleska AU - Michael Forsting AU - Ken Herrmann AU - Lale Umutlu TI - <sup>18</sup>F-FDG PET/MRI for Therapy Response Assessment of Isolated Limb Perfusion in Patients with Soft-Tissue Sarcomas AID - 10.2967/jnumed.119.226761 DP - 2019 Nov 01 TA - Journal of Nuclear Medicine PG - 1537--1542 VI - 60 IP - 11 4099 - http://jnm.snmjournals.org/content/60/11/1537.short 4100 - http://jnm.snmjournals.org/content/60/11/1537.full SO - J Nucl Med2019 Nov 01; 60 AB - Our purpose was to assess the diagnostic potential of simultaneously acquired 18F-FDG PET and MRI data sets for therapy response assessment of isolated limb perfusion (ILP) in patients with soft-tissue sarcomas (STS). Methods: In total, 45 patients with histopathologically verified STS were prospectively enrolled for an integrated 18F-FDG PET/MRI examination before and after ILP. Therapy response was assessed based on different MRI- and PET-derived morphologic (RECIST and the MR-adapted Choi criteria) and metabolic (PERCIST) criteria. In addition, a regression model was used combining relative changes in quantitative variables to predict treatment response under ILP. Histopathologic results after subsequent tumor resection served as the reference standard, and patients were categorized as responders or nonresponders on the basis of the 6-stage regression scale by Salzer-Kuntschik. Results: Histopathologic analysis categorized 27 patients as responders (grades I–III) and 18 patients as nonresponders (grades IV–VI). Calculated sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy were 22%, 89%, 75%, 43%, and 49% for RECIST; 70%, 44%, 66%, 50%, and 60% for the Choi criteria; and 85%, 78%, 85%, 78%, and 82% for PERCIST. Receiver-operating-characteristic analysis revealed an area under the curve (AUC) of 0.56 for RECIST, 0.57 for the Choi criteria, and 0.82 for PERCIST. The combined regression model revealed higher values (AUC, 0.90) than for the stand-alone analysis, however, differences to metabolic parameters did not reach significance (P value: 0.067). Conclusion: Our study demonstrates the superiority of 18F-FDG PET over MRI data sets for response assessment of STS under neoadjuvant ILP. In a clinical setting, MRI delivers valuable information for presurgical assessment. Therefore, combining 18F-FDG PET and MRI data may enable more reliable treatment planning and therapy monitoring of STS.