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Harder, F.N.* ; Jungmann, F.* ; Kaissis, G.* ; Lohöfer, F.K.* ; Ziegelmayer, S.* ; Havel, D.* ; Quante, M.* ; Reichert, M.* ; Schmid, R.M.* ; Demir, I.E.* ; Friess, H.* ; Wildgruber, M.* ; Siveke, J.* ; Muckenhuber, A.* ; Steiger, K.* ; Weichert, W.* ; Rauscher, I.* ; Eiber, M.* ; Makowski, M.R.* ; Braren, R.F.*

[18F]FDG PET/MRI enables early chemotherapy response prediction in pancreatic ductal adenocarcinoma.

EJNMMI Res. 11:70 (2021)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
PURPOSE: In this prospective exploratory study, we evaluated the feasibility of [18F]fluorodeoxyglucose ([18F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset. MATERIAL AND METHODS: In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [18F]FDG PET/MRI before and two weeks after onset of chemotherapy. Response per RECIST1.1 was then assessed at 3 months [18F]FDG PET/MRI-derived parameters (MTV50%, TLG50%, MTV2.5, TLG2.5, SUVmax, SUVpeak, ADCmax, ADCmean and ADCmin) were assessed, using multiple t-test, Man-Whitney-U test and Fisher's exact test for binary features. RESULTS: At 72 ± 43 days, twelve patients were classified as responders and five patients as non-responders. An increase in ∆MTV50% and ∆ADC (≥ 20% and 15%, respectively) and a decrease in ∆TLG50% (≤ 20%) at 2 weeks after chemotherapy onset enabled prediction of responders and non-responders, respectively. Parameter combinations (∆TLG50% and ∆ADCmax or ∆MTV50% and ∆ADCmax) further improved discrimination. CONCLUSION: Multiparametric [18F]FDG PET/MRI-derived parameters, in particular indicators of a change in tumor glycolysis and cellularity, may enable very early chemotherapy response prediction. Further prospective studies in larger patient cohorts are recommended to their clinical impact.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Chemotherapy ; Pdac ; Pet/mri ; Response Prediction
Sprache englisch
Veröffentlichungsjahr 2021
HGF-Berichtsjahr 2021
ISSN (print) / ISBN 2191-219X
e-ISSN 2191-219X
Zeitschrift EJNMMI Research
Quellenangaben Band: 11, Heft: 1, Seiten: , Artikelnummer: 70 Supplement: ,
Verlag Springer
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530014-001
Förderungen Fakultät für Medizin, Technische Universität München
Deutsche Forschungsgemeinschaft
DOI 10.1186/s13550-021-00808-4
PubMed ID 34322781
Erfassungsdatum 2022-09-13
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