Extracapsular tumor spread and the risk of local, axillary and supraclavicular recurrence in node-positive, premenopausal patients with breast cancer

BACKGROUND: Extracapsular tumor spread (ECS) has been identified as a possible risk factor for breast cancer recurrence, but controversy exists regarding its role in decision making for regional radiotherapy. This study evaluates ECS as a predictor of local, axillary, and supraclavicular recurrence. PATIENTS AND METHODS: International Breast Cancer Study Group Trial VI accrued 1475 eligible pre- and perimenopausal women with node-positive breast cancer who were randomly assigned to receive three to nine courses of classical combination chemotherapy with cyclophosphamide, methotrexate, and fluorouracil. ECS status was determined retrospectively in 933 patients based on review of pathology reports. Cumulative incidence and hazard ratios (HRs) were estimated using methods for competing risks analysis. Adjustment factors included treatment group and baseline patient and tumor characteristics. The median follow-up was 14 years. RESULTS: In univariable analysis, ECS was significantly associated with supraclavicular recurrence (HR = 1.96; 95% confidence interval 1.23-3.13; P = 0.005). HRs for local and axillary recurrence were 1.38 (P = 0.06) and 1.81 (P = 0.11), respectively. Following adjustment for number of lymph node metastases and other baseline prognostic factors, ECS was not significantly associated with any of the three recurrence types studied. CONCLUSIONS: Our results indicate that the decision for additional regional radiotherapy should not be based solely on the presence of ECS.

Is adjuvant chemotherapy of benefit for postmenopausal women who receive endocrine treatment for highly endocrine-responsive, node-positive breast cancer? International Breast Cancer Study Group Trials VII and 12-93

To compare the efficacy of chemoendocrine treatment with that of endocrine treatment (ET) alone for postmenopausal women with highly endocrine responsive breast cancer. In the International Breast Cancer Study Group (IBCSG) Trials VII and 12-93, postmenopausal women with node-positive, estrogen receptor (ER)-positive or ER-negative, operable breast cancer were randomized to receive either chemotherapy or endocrine therapy or combined chemoendocrine treatment. Results were analyzed overall in the cohort of 893 patients with endocrine-responsive disease, and according to prospectively defined categories of ER, age and nodal status. STEPP analyses assessed chemotherapy effect. The median follow-up was 13 years. Adding chemotherapy reduced the relative risk of a disease-free survival event by 19% (P = 0.02) compared with ET alone. STEPP analyses showed little effect of chemotherapy for tumors with high levels of ER expression (P = 0.07), or for the cohort with one positive node (P = 0.03). Chemotherapy significantly improves disease-free survival for postmenopausal women with endocrine-responsive breast cancer, but the magnitude of the effect is substantially attenuated if ER levels are high.

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).