% IMPORTANT: The following is UTF-8 encoded. This means that in the presence % of non-ASCII characters, it will not work with BibTeX 0.99 or older. % Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or % “biber”. @ARTICLE{Hussein:817964, author = {Hussein, Amr and Furth, Christian and Schönberger, Stefan and Hundsdoerfer, Patrick and Steffen, Ingo and Amthauer, Holger and Müller, Hans-Wilhelm and Hautzel, Hubertus}, title = {{FDG}-{PET} {R}esponse {P}rediction in {P}ediatric {H}odgkin’s {L}ymphoma: {I}mpact of {M}etabolically {D}efined {T}umor {V}olumes and {I}ndividualized {SUV} {M}easurements on the {P}ositive {P}redictive {V}alue}, journal = {Cancers}, volume = {7}, number = {1}, issn = {2072-6694}, address = {Basel}, publisher = {MDPI}, reportid = {FZJ-2016-04543}, pages = {287 - 304}, year = {2015}, abstract = {Background: In pediatric Hodgkin’s lymphoma (pHL) early response-to-therapy prediction is metabolically assessed by (18)F-FDG PET carrying an excellent negative predictive value (NPV) but an impaired positive predictive value (PPV). Aim of this study was to improve the PPV while keeping the optimal NPV. A comparison of different PET data analyses was performed applying individualized standardized uptake values (SUV), PET-derived metabolic tumor volume (MTV) and the product of both parameters, termed total lesion glycolysis (TLG); Methods: One-hundred-eight PET datasets (PET1, n = 54; PET2, n = 54) of 54 children were analysed by visual and semi-quantitative means. SUVmax, SUVmean, MTV and TLG were obtained the results of both PETs and the relative change from PET1 to PET2 (Δ in $\%)$ were compared for their capability of identifying responders and non-responders using receiver operating characteristics (ROC)-curves. In consideration of individual variations in noise and contrasts levels all parameters were additionally obtained after threshold correction to lean body mass and background; Results: All semi-quantitative SUV estimates obtained at PET2 were significantly superior to the visual PET2 analysis. However, ΔSUVmax revealed the best results (area under the curve, 0.92; p < 0.001; sensitivity $100\%;$ specificity $85.4\%;$ PPV $46.2\%;$ NPV $100\%;$ accuracy, $87.0\%)$ but was not significantly superior to SUVmax-estimation at PET2 and ΔTLGmax. Likewise, the lean body mass and background individualization of the datasets did not impove the results of the ROC analyses; Conclusions: Sophisticated semi-quantitative PET measures in early response assessment of pHL patients do not perform significantly better than the previously proposed ΔSUVmax. All analytical strategies failed to improve the impaired PPV to a clinically acceptable level while preserving the excellent NPV.}, cin = {KME / INM-4}, ddc = {610}, cid = {I:(DE-Juel1)KME-20110218 / I:(DE-Juel1)INM-4-20090406}, pnm = {573 - Neuroimaging (POF3-573)}, pid = {G:(DE-HGF)POF3-573}, typ = {PUB:(DE-HGF)16}, pubmed = {pmid:25635760}, UT = {WOS:000209951000010}, doi = {10.3390/cancers7010287}, url = {https://juser.fz-juelich.de/record/817964}, }