Prognostic factors of recurrence and disease-free survival in radically resected pulmonary carcinoids: a real-world analysis.

Background: Pulmonary carcinoids (PCs) are rare neuroendocrine lung tumors which may recur, thus worsening their otherwise favorable overall prognosis. Aiming to identify patients at risk for recurrence, we examined parameters affecting disease-free survival (DFS). Methods: A retrospective single-center analysis of 82 consecutive patients undergoing curative intent resection for primary PC tumors between 2010 and 2019 was carried out. Kaplan-Meier method was utilized for survival analysis. Independent prognostic factors were determined using multivariable Cox and logistic regression. Results: During the observation period 82 patients, 48 females (58.5%) and 34 males (41.5%) were operated, representing 84 cases of PCs, 56 typical (TCs) (66.7%) and 28 atypical (ACs) (33.3%) carcinoids. Five-year overall survival was 87.5% and 84.7%, 5-year DFS 97.5% and 74.9% (P=0.012) for TCs and ACs, respectively. Recurrences occurred in one patient (1.8%) with TCs and five patients (17.9%) with ACs (P=0.014). Using multivariable Cox regression, tumor size (cm) remained as an independent prognostic factor for reduced DFS (P=0.018). In logistic regression, nodal involvement (P=0.043) and tumor size (cm) (P=0.023) were independently associated with higher risk of recurrence. Age, sex, smoking, location, and Ki-67 index were not independently associated with recurrence or DFS. Conclusions: Recurrence in PCs after complete resection is relatively rare. However, DFS is reduced in ACs compared to TCs. Tumor size (cm) and nodal involvement appear as the most important prognostic factors associated with recurrence in PCs, independent of histologic type.

Identification of heterogeneous aquifer transmissivity using an AE-based method.

Determination of hydraulic head, H, as a function of spatial coordinates and time, in ground water flow is the basis for aquifer management and for prediction of contaminant transport. Several computer codes are available for this purpose. Spatial distribution of the transmissivity, T(x,y), is a required input to these codes. In most aquifers, T varies in an erratic manner, and it can be characterized statistically in terms of a few moments: the expected value, the variance, and the variogram. Knowledge of these moments, combined with a few measurements, permits one to estimate T at any point using geostatistical methods. In a review of transmissivity data from 19 unconsolidated aquifers, Hoeksema and Kitanidis (1985) identified two types of the logtransmissivity Y= ln(T) variations: correlated variations with variance sigma2Yc and correlation scale, I(Y), on the order of kilometers, and uncorrelated variations with variance sigma2Yn. Direct identification of the logtransmissivity variogram, Gamma(Y), from measurements is difficult because T data are generally scarce. However, many head measurements are commonly available. The aim of the paper is to introduce a methodology to identify the transmissivity variogram parameters (sigma2Yc, I(Y), and sigma2Yn) using head data in formations characterized by large logtransmissivity variance. The identification methodology uses a combination of precise numerical simulations (carried out using analytic element method) and a theoretical model. The main objective is to demonstrate the application of the methodology to a regional ground water flow in Eagle Valley basin in west-central Nevada for which abundant transmissivity and head measurements are available.

The nested superblock approach for regional-scale analytic element models.

A new approach is presented for improving the computational efficiency of regional-scale ground water models based on the analytic element method (AEM). The algorithm is an extension of the existing "superblock" algorithm, which combines the effects of multiple analytic elements into Laurent series and Taylor series (superblock expansions). With the new "nested superblock" formulation, Laurent series are nested in a hierarchical (quad-tree) data structure with direct mathematical relationships between parent and child superblock coefficients. Nested superblocks significantly accelerate the evaluation of the complex potential and discharge function in models that contain a large number of analytic elements at multiple scales. This evaluation process, the primary computational cost of AEM models, is required to determine the element coefficients, generate contour plots, and trace pathlines. The performance of the nested superblocks is demonstrated with a simplified model based on the Lake Ontario watershed geometry comprising thousands of hydrogeologic features at multiple geographic scales.

Effective conductivity of heterogeneous multiphase media with circular inclusions.

Determining the effective conductivity of heterogeneous media is a central problem in different fields of physics. The medium considered here contains cylinders (inclusions) of random conductivities that are distributed at random in an embedding matrix. For random systems, widely encountered in applications, we derive an approximative analytical solution that applies to significantly denser configurations than Maxwell first-order approximations. The analytic solution is tested against accurate numerical simulations. The widely used effective medium approach is shown to be exact for symmetric conductivity distributions and quite accurate for asymmetrical cases.