Intra arterial treatment of hepatocellular carcinoma: Comparison of MELD score variations between radio-embolization and chemo-embolization.

PURPOSE: The purpose of this study was to assess liver function deterioration, as assessed using the model for end-stage liver disease (MELD) score variations, following transarterial chemo-embolization (TACE) versus selective internal radiation therapy (SIRT) in patients with unresectable unilobar hepatocellular carcinomas (HCC). PATIENTS AND METHODS: We retrospectively evaluated all patients who underwent a single conventional TACE or SIRT procedure in our department from May 2013 to May 2018 for unilobar unresectable HCC. A total of 86 patients (76 men, 20 women; mean age, 65.5 years) were included. There were 63 patients in the TACE group [56 men, 7 women; mean age, 65.1±9.6 (SD) years] and 23 patients in the SIRT group [20 men, 3 women; mean age, 70±9.2 (SD) years]. Delta MELD, defined as post treatment minus pre-treatment MELD score, was considered for liver function deterioration and compared between patients who underwent single lobar treatment of SIRT versus TACE. RESULTS: Patients in SIRT group had significant higher tumor burden, alpha-fetoprotein serum level, and rates of macroscopic vessel invasion. Mean pre-treatment MELD scores did not differ between TACE [mean, 8.41±1.71 (SD); range: 7.24-9.24] and SIRT groups [mean, 8.36±1.74 (SD); range: 7.07-9.21] (P=0.896) as well as Child-Pugh class and albumin-bilirubin (ALBI) grade distribution. However, following treatment, mean DeltaMELD was greater in TACE group (mean, 0.83±1.83 [SD]; range: -0.30--1.31) than in SIRT group (mean, -0.13±1.06 [SD]; range: -0.49-0.32) (P=0.021). At multivariate analysis, SIRT treatment was independently associated with a lower DeltaMELD score than TACE (R=-0.955 [-1.68; -0.406]; P=0.017;). CONCLUSION: Whereas performed in patients with higher tumor burden, SIRT resulted in lower degrees of liver function worsening as assessed using MELD score variations.

Confidence Interval Constraint-Based Regularization Framework for PET Quantization.

In this paper, a new generic regularized reconstruction framework based on confidence interval constraints for tomographic reconstruction is presented. As opposed to usual state-of-the-art regularization methods that try to minimize a cost function expressed as the sum of a data-fitting term and a regularization term weighted by a scalar parameter, the proposed algorithm is a two-step process. The first step concentrates on finding a set of images that rely on the direct estimation of confidence intervals for each reconstructed value. Then, the second step uses confidence intervals as a constraint to choose the most appropriate candidate according to a regularization criterion. Two different constraints are proposed in this paper. The first one has the main advantage of strictly ensuring that the regularized solution will respect the interval-valued data-fitting constraint, thus preventing over-smoothing of the solution while offering interesting properties in terms of spatial and statistical bias/variance trade-off. Another regularization proposition based on the design of a smoother constraint also with appealing properties is proposed as an alternative. The competitiveness of the proposed framework is illustrated in comparison to other regularization schemes using analytical and GATE-based simulation and real PET acquisition.

Least-squares dual characterization for ROI assessment in emission tomography.

Our aim is to describe an original method for estimating the statistical properties of regions of interest (ROIs) in emission tomography. Drawn upon the works of Louis on the approximate inverse, we propose a dual formulation of the ROI estimation problem to derive the ROI activity and variance directly from the measured data without any image reconstruction. The method requires the definition of an ROI characteristic function that can be extracted from a co-registered morphological image. This characteristic function can be smoothed to optimize the resolution-variance tradeoff. An iterative procedure is detailed for the solution of the dual problem in the least-squares sense (least-squares dual (LSD) characterization), and a linear extrapolation scheme is described to compensate for sampling partial volume effect and reduce the estimation bias (LSD-ex). LSD and LSD-ex are compared with classical ROI estimation using pixel summation after image reconstruction and with Huesman's method. For this comparison, we used Monte Carlo simulations (GATE simulation tool) of 2D PET data of a Hoffman brain phantom containing three small uniform high-contrast ROIs and a large non-uniform low-contrast ROI. Our results show that the performances of LSD characterization are at least as good as those of the classical methods in terms of root mean square (RMS) error. For the three small tumor regions, LSD-ex allows a reduction in the estimation bias by up to 14%, resulting in a reduction in the RMS error of up to 8.5%, compared with the optimal classical estimation. For the large non-specific region, LSD using appropriate smoothing could intuitively and efficiently handle the resolution-variance tradeoff.

A heuristic statistical stopping rule for iterative reconstruction in emission tomography.

OBJECTIVE: We propose a statistical stopping criterion for iterative reconstruction in emission tomography based on a heuristic statistical description of the reconstruction process. METHODS: The method was assessed for MLEM reconstruction. Based on Monte-Carlo numerical simulations and using a perfectly modeled system matrix, our method was compared with classical iterative reconstruction followed by low-pass filtering in terms of Euclidian distance to the exact object, noise, and resolution. The stopping criterion was then evaluated with realistic PET data of a Hoffman brain phantom produced using the GATE platform for different count levels. RESULTS: The numerical experiments showed that compared with the classical method, our technique yielded significant improvement of the noise-resolution tradeoff for a wide range of counting statistics compatible with routine clinical settings. When working with realistic data, the stopping rule allowed a qualitatively and quantitatively efficient determination of the optimal image. CONCLUSIONS: Our method appears to give a reliable estimation of the optimal stopping point for iterative reconstruction. It should thus be of practical interest as it produces images with similar or better quality than classical post-filtered iterative reconstruction with a mastered computation time.