Identificazione e dosaggio di marcatori molecolari dell'endometriosi nel sangue periferico

Purpose: to quantify the mRNA levels of MMP-3, MMP-9, VEGF and Survivin in peripheral blood and the serum levels of CA-125, Ca19-9 in women with and without endometriosis and to investigate the performance of these markers to differentiate between deep and ovarian endometriosis. Methods: a case controls study enrolled a series of 60 patients. Twenty controls have been matched with 20 cases of ovarian and 20 cases of deep endometriosis. Univariable and multivariable performance of serum CA125 and CA19-9, mRNA for Survivin, MMP9, MMP3 and VEGF genes have been evaluated by means of ROC curves and logistic regression respectively. Results: No difference in markers concentration were detected between ovarian and deep endometriosis. In comparison with controls serum CA19 and CA125 yielded the better sensitivity followed by mRNA for Survivin gene (81.5%, 51.9% and 7.5% at 10% false positive rate respectively). Multivariable estimated odds of endometriosis yielded a sensitivity of 87% at the same false positive rate. Conclusions: A combination of serum and molecular markers could allow a better diagnosis of endometriosis.

Cellular Proliferation in the Prognosis of Intermediate Grade Mast Cell Tumour in Dogs

This was a retrospective study including ninety samples of dogs with a histological diagnosis of intermediate grade cutaneous mast cell tumour (MCT). The objectives of the study were to validate Minichromosome Maintenance Protein 7 (MCM7) as a prognostic marker in MCTs and to compare the ability of mitotic index (MI), Ki67 and MCM7 to predict outcome. The median survival for the entire population was not reached at 2099 days. The mean survival time was 1708 days. Seventy-two cases were censored after a median follow up of 1136 days and eighteen dogs died for causes related to the MCT after a median of 116 days. For each sample MI, Ki67 and MCM7 were determined. The Receiver Operating Characteristic (ROC) curve was obtained for each prognostic marker to evaluate the performance of the test, expressed as area under the curve, and whether the published threshold value was adequate. Kaplan-Meier and corresponding logrank test for MI, Ki67 and MCM7 as binary variables was highly significant (P<0.0001). Multivariable regression analysis of MI, Ki67 and MCM7 corrected for age and surgical margins indicated that the higher risk of dying of MCT was associated with MCM7 > 0.18 (Hazard Ration [HR] 14.7; P<0.001) followed by MI > 5 (HR 13.9; P<0.001) and Ki67 > 0.018 (HR 8.9; P<0.001). Concluding, the present study confirmed that MCM7 is an excellent prognostic marker in cutaneous MCTs being able to divide Patnaik intermediate grade tumours in two categories with different prognosis. Ki67 was equally good confirming its value as a prognostic marker in intermediate grade MCTs. The mitotic index was extremely specific, but lacked of sensitivity. Interestingly, mitotic index, Ki67 and MCM7 were independent from each other suggesting that their combination would improve their individual prognostic value.

Prediction Nomogram for 68Ga-PSMA-11 PET/CT in different clinical settings of PSA failure after radical treatment for Prostate Cancer

Objective The objective of this study was to develop a clinical nomogram to predict gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (68Ga-PSMA-11-PET/CT) positivity in different clinical settings of PSA failure. Materials and methods Seven hundred three (n = 703) prostate cancer (PCa) patients with confirmed PSA failure after radical therapy were enrolled. Patients were stratified according to different clinical settings (first-time biochemical recurrence [BCR]: group 1; BCR after salvage therapy: group 2; biochemical persistence after radical prostatectomy [BCP]: group 3; advanced stage PCa before second-line systemic therapies: group 4). First, we assessed 68Ga-PSMA-11-PET/CT positivity rate. Second, multivariable logistic regression analyses were used to determine predictors of positive scan. Third, regression-based coefficients were used to develop a nomogram predicting positive 68Ga-PSMA-11-PET/CT result and 200 bootstrap resamples were used for internal validation. Fourth, receiver operating characteristic (ROC) analysis was used to identify the most informative nomogram’s derived cut-off. Decision curve analysis (DCA) was implemented to quantify nomogram’s clinical benefit. Results 68Ga-PSMA-11-PET/CT overall positivity rate was 51.2%, while it was 40.3% in group 1, 54% in group 2, 60.5% in group 3, and 86.9% in group 4 (p < 0.001). At multivariable analyses, ISUP grade, PSA, PSA doubling time, and clinical setting were independent predictors of a positive scan (all p ≤ 0.04). A nomogram based on covariates included in the multivariate model demonstrated a bootstrap-corrected accuracy of 82%. The nomogram-derived best cut-off value was 40%. In DCA, the nomogram revealed clinical net benefit of > 10%. Conclusions This novel nomogram proved its good accuracy in predicting a positive scan, with values ≥ 40% providing the most informative cut-off in counselling patients to 68Ga-PSMA-11-PET/CT. This tool might be important as a guide to clinicians in the best use of PSMA-based PET imaging.

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

The main topic of this thesis is confounding in linear regression models. It arises when a relationship between an observed process, the covariate, and an outcome process, the response, is influenced by an unmeasured process, the confounder, associated with both. Consequently, the estimators for the regression coefficients of the measured covariates might be severely biased, less efficient and characterized by misleading interpretations. Confounding is an issue when the primary target of the work is the estimation of the regression parameters. The central point of the dissertation is the evaluation of the sampling properties of parameter estimators. This work aims to extend the spatial confounding framework to general structured settings and to understand the behaviour of confounding as a function of the data generating process structure parameters in several scenarios focusing on the joint covariate-confounder structure. In line with the spatial statistics literature, our purpose is to quantify the sampling properties of the regression coefficient estimators and, in turn, to identify the most prominent quantities depending on the generative mechanism impacting confounding. Once the sampling properties of the estimator conditionally on the covariate process are derived as ratios of dependent quadratic forms in Gaussian random variables, we provide an analytic expression of the marginal sampling properties of the estimator using Carlson’s R function. Additionally, we propose a representative quantity for the magnitude of confounding as a proxy of the bias, its first-order Laplace approximation. To conclude, we work under several frameworks considering spatial and temporal data with specific assumptions regarding the covariance and cross-covariance functions used to generate the processes involved. This study allows us to claim that the variability of the confounder-covariate interaction and of the covariate plays the most relevant role in determining the principal marker of the magnitude of confounding.

Seemingly unrelated linear regression for contaminated data based on Gaussian mixtures

In this thesis, new classes of models for multivariate linear regression defined by finite mixtures of seemingly unrelated contaminated normal regression models and seemingly unrelated contaminated normal cluster-weighted models are illustrated. The main difference between such families is that the covariates are treated as fixed in the former class of models and as random in the latter. Thus, in cluster-weighted models the assignment of the data points to the unknown groups of observations depends also by the covariates. These classes provide an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that allows to specify a different vector of regressors for the prediction of each response. Expectation-conditional maximisation algorithms for the calculation of the maximum likelihood estimate of the model parameters have been derived. As the number of free parameters incresases quadratically with the number of responses and the covariates, analyses based on the proposed models can become unfeasible in practical applications. These problems have been overcome by introducing constraints on the elements of the covariance matrices according to an approach based on the eigen-decomposition of the covariance matrices. The performances of the new models have been studied by simulations and using real datasets in comparison with other models. In order to gain additional flexibility, mixtures of seemingly unrelated contaminated normal regressions models have also been specified so as to allow mixing proportions to be expressed as functions of concomitant covariates. An illustration of the new models with concomitant variables and a study on housing tension in the municipalities of the Emilia-Romagna region based on different types of multivariate linear regression models have been performed.