Finite mixture model-based classification of a complex vegetation system

To propose a Finite Mixture Model (FMM) as an additional approach for classifying large datasets of georeferenced vegetation plots from complex vegetation systems. Study area: The Italian peninsula including the two main islands (Sicily and Sardinia), but excluding the Alps and the Po plain. Methods: We used a database of 5,593 georeferenced plots and 1,586 vascular species of forest vegetation, created in TURBOVEG by storing published and unpublished phytosociological plots collected over the last 30 years. The plots were classified according to species composition and environmental variables using a FMM. Classification results were compared with those obtained by TWINSPAN algorithm. Groups were characterized in terms of ecological parameters, dominant and diagnostic species using the fidelity coefficient. Interpretation of resulting forest vegetation types was supported by a predictive map, produced using discriminant functions on environmental predictors, and by a non\u2010metric multidimensional scaling ordination. Results: FMM clustering obtained 24 groups that were compared with those from TWINSPAN, and similarities were found only at a higher classification level corresponding to the main orders of the Italian broadleaf forest vegetation: Fagetalia sylvaticae, Carpinetalia betuli, Quercetalia pubescenti-petraeae and Quercetalia ilicis. At lower syntaxonomic level, these 24 groups were referred to alliances and sub-alliances. Conclusions: Despite a greater computational complexity, FMM appears to be an effective alternative to the traditional classification methods through the incorporation of modelling in the classificatory process. This allows classification of both the co-occurrence of species and environmental factors so that groups are identified not only on their species composition, as in the case of TWINSPAN, but also on their specific environmental niche

Real-World Performance of the American Thyroid Association Risk Estimates in Predicting 1-Year Differentiated Thyroid Cancer Outcomes: A Prospective Multicenter Study of 2000 Patients

Background: One of the most widely used risk stratification systems for estimating individual patients' risk of persistent or recurrent differentiated thyroid cancer (DTC) is the American Thyroid Association (ATA) guidelines. The 2015 ATA version, which has increased the number of patients considered at low or intermediate risk, has been validated in several retrospective, single-center studies. The aims of this study were to evaluate the real-world performance of the 2015 ATA risk stratification system in predicting the response to treatment 12 months after the initial treatment and to determine the extent to which this performance is affected by the treatment center in which it is used. Methods: A prospective cohort of DTC patients collected by the Italian Thyroid Cancer Observatory web-based database was analyzed. We reviewed all records present in the database and selected consecutive cases that satisfied inclusion criteria: (i) histological diagnosis of DTC, with the exclusion of noninvasive follicular thyroid neoplasm with papillary-like nuclear features; (ii) complete data of the initial treatment and pathological features; and (iii) results of 1-year follow-up visit (6-18 months after the initial treatment), including all data needed to classify the estimated response to treatment. Results: The final cohort was composed of 2071 patients from 40 centers. The ATA risk of persistent/recurrent disease was classified as low in 1109 patients (53.6%), intermediate in 796 (38.4%), and high in 166 (8.0%). Structural incomplete responses were documented in only 86 (4.2%) patients: 1.5% in the low-risk, 5.7% in the intermediate-risk, and 14.5% in the high-risk group. The baseline ATA risk class proved to be a significant predictor of structural persistent disease, both for intermediate-risk (odds ratio [OR] 4.67; 95% confidence interval [CI] 2.59-8.43) and high-risk groups (OR 16.48; CI 7.87-34.5). Individual center did not significantly influence the prediction of the 1-year disease status. Conclusions: The ATA risk stratification system is a reliable predictor of short-term outcomes in patients with DTC in real-world clinical settings characterized by center heterogeneity in terms of size, location, level of care, local management strategies, and resource availability