The combination of usual prognostic factors with appropriately chosen textural and shape parameters evaluated on baseline PET-CT improves the prediction of early metabolic response in bulky lymphoma.

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Objective The threefold aim was to (1) compare areal bone mineral density (aBMD), bone turnover markers, and periostin levels in young women with either anorexia nervosa (AN) or obesity (OB) and controls (CON); (2) model the profiles according to age; and (3) determine the parameters associated with aBMD. Subjects and Methods One hundred and fifty-two young women with ages ranging from 16.0 to 27.0 years were subdivided into 3 groups (AN, OB, CON). The CON group was age-matched by ±6 months. aBMD, bone turnover markers, and periostin levels were evaluated. Results aBMD modeling showed that hip aBMD was higher in OB than in the other 2 groups from 19 years, and AN presented lower values than CON from 21 years. aBMD at the lumbar spine was higher in older OB and CON women, starting from 20 to 22 years, but in AN the difference with the other 2 groups increased with age. Periostin levels were lower in OB than in AN or CON, but no variation with age was observed. Compared with controls, OB and AN presented similarly lower markers of bone formation, although markers of bone resorption were lower in OB and higher in AN. A modeling approach showed that markers of bone formation and resorption were lower in older than in younger CON, whereas the values of these bone markers remained relatively constant in AN and OB. In all groups, lean body mass (LBM) was the parameter most positively correlated with aBMD. Conclusion This study demonstrated that weight extremes (AN or OB) influence aBMD, bone remodeling and periostin profiles. Moreover, factors related to aBMD were specific to each condition, but LBM was the parameter most consistently associated with aBMD.

Purpose Dynamic 18F‐FDG PET allows quantitative estimation of cerebral glucose metabolism both at the regional and local (voxel) level. Although sensitive to noise and highly computationally expensive, nonlinear least‐squares (NLS) optimization stands as the reference approach for the estimation of the kinetic model parameters. Nevertheless, faster techniques, including linear least‐squares (LLS) and Patlak graphical method, have been proposed to deal with high resolution noisy data, representing a more adaptable solution for routine clinical implementation. Former research investigating the relative performance of the available algorithms lack precise evaluation of kinetic parameter estimates under realistic acquisition conditions. Methods The present study aims at the systematic comparison of the feasibility and pertinence of kinetic modeling of dynamic cerebral 18F‐FDG PET using NLS, LLS, and Patlak method, based on numerical simulations and patient data. Numerical simulations were used to study the bias and variance of K1 and Ki parameters estimation under representative noise levels. Patient data allowed to assess the concordance between the three methods at the regional and voxel scale, and to evaluate the robustness of the estimations with respect to patient head motion. Results and Conclusions Our findings indicate that at the regional level NLS and LLS provide kinetic parameter estimates (K1 and Ki) with similar bias and variance characteristics (K1 bias ± relative standard deviation [RSD] 0.0 ± 5.1% and 0.1% ± 4.9% for NLS and LLS respectively, Ki bias ± RSD 0.1% ± 4.5% and −0.7% ± 4.4% for NLS and LLS respectively). NLS estimates appear, however, to be slightly less sensitive to patient motion. At the voxel level, provided that patient motion is negligible or corrected, LLS offers an appealing alternative solution for local K1 mapping. It yields K1 estimates that are highly correlated, with high correlation with NLS values (Pearson's r = 0.95 on actual data) within computations times less than two orders of magnitude lower. Last, Patlak method appears as the most robust and accurate technique for the estimation of Ki values at the regional and voxel scale, with or without head motion. It provides low bias/low variance Ki quantification (bias ± RSD −1.5 ± 9.5% and −4.1 ± 19.7% for Patlak and NLS respectively) as well as smooth parametric images suitable for visual assessment.