Does axillary dissection affect prognosis in T1 breast tumors?

The treatment of patients with breast cancer has undergone many revisions over recent decades. The current trend is toward limited resections and breast conservation. Some authors advocate the abandonment of axillary lymph node dissection (ALND) for small tumors. While it is accepted that ALND has no therapeutic effect in breast cancer patients, its prognostic significance for small tumors is debated. Eligibility criteria for surgical treatment without axillary dissection are evolving. Considering that problem, we retrospectively reviewed the charts of 100 patients with T1 invasive carcinoma of the breast treated at Hippokration Hospital of Athens between 1986 and 1987. Patients were divided into two groups: those that underwent ALND (n=76) and those that did not (n=24). The following data were recorded: age, tumor size, grade, hormone receptor status and postoperative treatment. The ten-year overall and disease-free survival were analysed. A multivariate analysis was used to identify prognostic variables. There was no statistically significant difference in the ten-year overall and disease-free survival between the two groups. The univariate analysis showed that tumor size predicts both recurrence and survival. In the multivariate analysis tumor size was found to be an independent prognostic factor for overall survival. ALND did not influence the ten-year survival or the recurrence rate. Tumor size was the only statistically significant and independent prognostic factor for T1 breast cancer patients

Can expression of apoptosis genes, bcl-2 and bax, predict survival and responsiveness to chemotherapy in node-negative breast cancer patients?

Although the status of the axillary lymph nodes is widely accepted to be associated with prognosis in breast cancer patients, there is a need for biomarkers to be analyzed as indicators of responsiveness to treatment. The objective of this study was to test the hypothesis that the expression of apoptosis genes, bcl-2 and bax, predicts survival and responsiveness to chemotherapy in node-negative breast cancer patients. One hundred thirty premenopausal women with primary breast carcinoma were studied for the expression of bcl-2 and bax genes. The relationship between the expression of bcl-2 and bax proteins and a series of markers of known prognostic value [such as tumor size, nuclear grade, receptors of the steroid hormones estrogen (ER) and progesterone (PgR)]. The association of these proteins with survival and responsiveness to chemotherapy was also examined. Sixty (46%) and sixty-four (49%) breast cancer cases were found positive for bcl-2 and bax, respectively, as indicated by immunohistochemistry. A statistically significant association was found between expression of bcl-2 and tumor size (P = 0.001), low grade (grade I) (P = 0.002), positivity of ER (P = 0.001), positivity of PR (P = 0.03), and superior disease-free survival (DFS) (P = 0.04), and superior overall survival (OS) (P = 0.03). In contrast, no similar associations were observed for the bax gene. Overall, there was a trend toward an association between adjuvant chemotherapy and DFS (P = 0.08) and OS (P = 0.07). This trend became statistically significant when the patients were analyzed by individual gene expression. In bax-positive patients, chemotherapy improves 6-year DFS (P = 0.01) and OS (P = 0.03) while similar effects were not observed in the other subgroups of patients. Our results indicated that bcl-2 expression is associated with a number of favorable prognostic factors and better clinical outcome, while bax expression seems to have positive predictive value for responsiveness to chemotherapy in lymph node-negative breast cancer patients

Efficient methods for near-optimal sequential decision making under uncertainty

This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal sequential decisions under uncertainty about the environment. Due to the uncertainty, such algorithms must not only learn from their interaction with the environment but also perform as well as possible while learning is taking place. \ua9 2010 Springer-Verlag Berlin Heidelberg