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Biomedical subjects

Federico Ambrogi

Publications and source records attributed to Federico Ambrogi.

6 recordsLinked to original sources
PubMed2006

Axillary lymph node nanometastases are prognostic factors for disease-free survival and metastatic relapse in breast cancer patients.

PURPOSE: Early breast cancer presents with a remarkable heterogeneity of outcomes. Undetected, microscopic lymph node tumor deposits may account for a significant fraction of this prognostic diversity. Thus, we systematically evaluated the presence of lymph node tumor cell deposits<or=0.2 mm in diameter [pN0(i+), nanometastases] and analyzed their prognostic effect. EXPERIMENTAL DESIGN: Single-institution, consecutive patients with 8 years of median follow-up (n=702) were studied. To maximize chances of detecting micrometastases and nanometastases, whole-axilla dissections were analyzed. pN0 cases (n=377) were systematically reevaluated by lymph node (n=6676) step-sectioning and anticytokeratin immunohistochemical analysis. The risk of first adverse events and of distant relapse of bona fide pN0 patients was compared with that of pN0(i+), pN1mi, and pN1 cases. RESULTS: Minimal lymph node deposits were revealed in 13% of pN0 patients. The hazard ratio for all adverse events of pN0(i+) versus pN0(i-) was 2.51 (P=0.00019). Hazards of pN1mi and pN0(i+) cases were not significantly different. A multivariate Cox model showed a hazard ratio of 2.16 for grouped pN0(i+)/pN1mi versus pN0(i-) (P=0.0005). Crude cumulative incidence curves for metastatic relapse were also significantly different (Gray's test chi2=5.54, P=0.019). CONCLUSION: Nanometastases are a strong risk factor for disease-free survival and for metastatic relapse. These findings support the inclusion of procedures for nanometastasis detection in tumor-node-metastasis staging.

Adult↗
PubMed2006

Artificial neural network for the joint modelling of discrete cause-specific hazards.

OBJECTIVE: Artificial neural network (ANN) based regression methods have been introduced for modelling censored survival data to account for complex prognostic patterns. In the framework of ANN extensions of generalized linear models for survival data, PLANN is a partial logistic ANN, suitable for smoothed discrete hazard estimation as a function of time and covariates. An extension of PLANN for competing risks analysis (PLANNCR) is now proposed for discrete or grouped survival times, resorting to the multinomial likelihood. METHODS AND MATERIALS: PLANNCR is built by assigning input nodes to the explanatory variables with the time interval treated as an ordinal variable. The logistic function is used as activation for the hidden nodes of the network, whereas the softmax, which corresponds to the canonical link of generalized linear models for polytomous regression, is adopted for multiple output nodes, to provide a smoothed estimation of discrete conditional event probabilities for each event. The Kullback-Leibler distance is used as error function for the target vectors, amounting to half of the deviance of a multinomial logistic regression model. PLANNCR can jointly model non-linear, non-proportional and non-additive effects on cause-specific hazards (CSHs). The degree of smoothing is modulated by the number of hidden nodes and penalization of the error function (weight decay). Model optimisation is achieved by quasi-Newton algorithms, while non-linear cross-validation (NCV) and the Network Information Criterion (NIC) were adopted for model selection. PLANNCR was applied to data on 1793 women with primary invasive breast cancer, histologically N-, who underwent surgery at the Milan Cancer Institute between 1981 and 1986. RESULTS: Differential effects of covariates and time on the shape of the CSH for the three main failure causes, namely intra-breast tumor recurrences, distant metastases and contralateral breast cancer, have been enlightened. CONCLUSIONS: PLANNCR can be suitably adopted in an exploratory framework for a thorough evaluation of the disease dynamics in the presence of competing risks.

Adult↗
PubMed2006

Molecular subtyping of breast cancer from traditional tumor marker profiles using parallel clustering methods.

PURPOSE: Recent small-sized genomic studies on the identification of breast cancer bioprofiles have led to profoundly dishomogenous results. Thus, we sought to identify distinct tumor profiles with possible clinical relevance based on clusters of immunohistochemical molecular markers measured on a large, single institution, case series. EXPERIMENTAL DESIGN: Tumor biological profiles were explored on 633 archival tissue samples analyzed by immunohistochemistry. Five validated markers were considered, i.e., estrogen receptors (ER), progesterone receptors (PR), Ki-67/MIB1 as a proliferation marker, HER2/NEU, and p53 in their original scale of measurement. The results obtained were analyzed by three different clustering algorithms. Four different indices were then used to select the different profiles (number of clusters). RESULTS: The best classification was obtained creating four clusters. Notably, three clusters were identified according to low, intermediate, and high ER/PR levels. A further subdivision in two biologically distinct subtypes was determined by the presence/absence of HER2/NEU and of p53. As expected, the cluster with high ER/PR levels was characterized by a much better prognosis and response to hormone therapy compared to that with the lowest ER/PR values. Notably, the cluster characterized by high HER2/NEU levels showed intermediate prognosis, but a rather poor response to hormone therapy. CONCLUSIONS: Our results show the possibility of profiling breast cancers by means of traditional markers, and have novel clinical implications on the definition of the prognosis of cancer patients. These findings support the existence of a tumor subtype that responds poorly to hormone therapy, characterized by HER2/NEU overexpression.

Adult↗
PubMed2006

Bivariate statistical approach to evaluate laboratory performance by analysis of standard curves in an External Quality Assurance program for quantitative assays based on real-time PCR with Taq-Man probes.

Recently a revolutionary technique for quantitative PCR determination was introduced in diagnostic laboratories. To determine the influence of technical variability on the reliability of the quantitative assay, it is crucial to use External Quality Assurance (EQA) programs. An EQA program was developed in Italy to check the analytical performance of real-time PCR procedures based on Taq-Mantrade mark probes. This article suggests a new statistical approach to discriminate, using a bivariate technique, laboratory performance that appears to be questionable, by separately considering the two main features of the standard curve: analytical sensitivity and efficiency. Furthermore, specific indexes to evaluate the impact of these two features on the determination of the initial number of molecules are given to help to improve the assay procedure.

Chemistry, Clinical↗
PubMed2005

Multiple correspondence analysis in S-PLUS.

Multiple correspondence analysis (MCA) is a multivariate method for analyzing multidimensional contingency tables. General software procedures to perform MCA are available. Among them SAS Proc CORRESP, SPAD CORMU procedure and the mca function of the MASS library in S-PLUS are probably the most used. However, CORRESP and CORMU output is different from that of mca function. The aim of this short note is showing how to obtain from mca function results compatible with those achieved with SAS or SPAD. A modified code is proposed in order to obtain the same coordinate system computed by SAS and SPAD. Moreover, the computation of the contributions of the levels of the factors to the inertia explained by each axis, the squared cosine of each factor level and the re-evaluation of the inertia explained by each axis have been added in order to improve the interpretations of the results of the decomposition.

Programming Languages↗