Preoperative staging of rectal cancer with MRI: correlation with pathologic staging

[INTRODUCTION] An accurate preoperative rectal cancer staging is crucial to the correct management of the disease. Despite great controversy around this issue, pelvic magnetic resonance (RM) is said to be the imagiologic standard modality. This work aimed to evaluate magnetic resonance accuracy in preoperative rectal cancer staging comparing with the anatomopathological results. METHODS We calculated sensibility, specificity, positive (VP positive) and negative (VP negative) predictive values for each T and N. We evaluated the concordance between both methods of staging using the Cohen weighted K (Kw), and through ROC curves, we evaluated magnetic resonance accuracy in rectal cancer staging. RESULTS 41 patients met the inclusion criteria. We achieved an efficacy of 43.9% for T and 61% for N staging. The respective sensibility, specificity, positive and negative predictive values are 33.3%, 94.7%, 33.3% and 94.7% for T1; 62.5%, 32%, 37.0% and 57.1% for T2; 31.8%, 79%, 63.6% and 50% for T3 and 27.8%, 87%, 62.5% and 60.6% for N. We obtained a poor concordance for T and N staging and the anatomopathological results. The ROC curves indicated that magnetic resonance is ineffective in rectal cancer staging. CONCLUSION Magnetic resonance has a moderate efficacy in rectal cancer staging and the major difficulty is in differentiating T2 and T3.

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.