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Laura Antolini

Publications and source records attributed to Laura Antolini.

3 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↗
PubMed2005

A time-dependent discrimination index for survival data.

To derive models suitable for outcome prediction, a crucial aspect is the availability of appropriate measures of predictive accuracy, which have to be usable for a general class of models. The Harrell's C discrimination index is an extension of the area under the ROC curve to the case of censored survival data, which owns a straightforward interpretability. For a model including covariates with time-dependent effects and/or time-dependent covariates, the original definition of C would require the prediction of individual failure times, which is not generally addressed in most clinical applications. Here we propose a time-dependent discrimination index Ctd where the whole predicted survival function is utilized as outcome prediction, and the ability to discriminate among subjects having different outcome is summarized over time. Ctd is based on a novel definition of concordance: a subject who developed the event should have a less predicted probability of surviving beyond his/her survival time than any subject who survived longer. The predicted survival function of a subject who developed the event is compared to: (1) that of subjects who developed the event before his/her survival time, and (2) that of subjects who developed the event, or were censored, after his/her survival time. Subjects who were censored are involved in comparisons with subjects who developed the event before their observed times. The index reduces to the previous C in the presence of separation between survival curves on the whole follow-up. A confidence interval for Ctd is derived using the jackknife method on correlated one-sample U-statistics.The proposed index is used to evaluate the discrimination ability of a model, including covariates having time-dependent effects, concerning time to relapse in breast cancer patients treated with adjuvant tamoxifen. The model was obtained from 596 patients entered prospectively at Istituto Nazionale per lo Studio e la Cura dei Tumori di Milano (INT). The model discrimination ability was validated on an independent testing data set of 175 patients provided by Centro Regionale Indicatori Biochimici di Tumore (CRIBT) in Venice.

Breast Neoplasms↗