70 Background: To assess outcomes of ER-negative breast CA pts treated with APBI, a matched-pair analysis was performed to determine efficacy of APBI vs whole breast RT (WBRT) from a single institution. Methods: From over 1650 pts treated with BCT from 1980-2013, a cohort of ER[-] pts treated with APBI or WBRT were investigated. Matched-pair analysis with a 1:1 ratio paired 79 APBI with 79 WBRT pts, all ER[-] (total:158). Match criteria included follow-up (FU) > 1.0 yr, stage, & age +/- 5 yrs. Outcomes analyzed included local recurrence (LR), true recurrence/marginal miss (TRMM), regional recurrence (RR), distant metastases (DM), disease-free (DFS), cause-specific (CSS), and overall survivals (OS). Results: As for clinical-pathological traits, no significant differences were noted for age (p=0.302), T-stage (p=1.000), tumor size (p=0.721), N-stage (p=0.062), use of chemoRx (p=0.747), endocrine Rx(p=0.408) or Herceptin (p=1.00). Per ASTRO Guidelines, no differences were seen in cautionary or unsuitable [UnS] groups between APBI & WBRT (p=0.333). With a mean FU of 8.0 yrs (10.1 yrs APBI; 8.4 yrs WBRT p<0.001), no differences were seen in the 10-yr actuarial rates of LR (9.3% vs 22.1% p=0.094), RR (1.3% vs 8.1% p=0.299), DM (7.1% vs 13.0% p=0.429), DFS (83.9% vs. 72.5% p=0.214), CSS (93.5% vs. 89.0 % p=0.677), or OS (79.6% vs. 80.1% p=0.573) between APBI & WBRT. Only TRMM was significantly different (0% APBI vs 12.5% p=0.011). In stratifying patients based on ER% (0%, 1-3%, 4-8%) no outcome differences were noted. Of the 158 ER[-] pts, 124 were cautionary with similar 10-yr outcomes except for TRMM (0% APBI;WBRT 14.4% p=0.017) & CLBF (0% APBI;WBRT17.1% p=0.019). For the 34 UnS patients, no endpoint differences were seen APBI vs WBRT. But, when the entire 158 ER[-] patients were analyzed for # of UnS factors, increasing UnS factors led to significant risk of RR (p<0.001) & DM (p=0.002). Conclusions: With 10-year FU of APBI for ER[-], the clinical results were equivalent to WBRT. No differences were noted based on ER%. Increasing number of unsuitable factors had more RR and DM. Maturation of randomized trial data will be needed to provide Class I evidence for equivalence of APBI to WBRT in ER[-] patients.
Limited long-term data exist regarding outcomes for patients treated with accelerated partial breast irradiation (APBI), particularly, when stratified by American Society for Radiation Oncology (ASTRO) Consensus Statement (CS) risk groups. The purpose of this analysis is to present 5- and 7-year outcomes following APBI based on CS groupings.A total of 690 patients with early-stage breast cancer underwent APBI from 1993 to 2012, receiving interstitial brachytherapy (n=195), balloon-based brachytherapy (n=290), or 3-dimensional conformal radiotherapy (n=205) at a single institution. Patients were stratified into suitable, cautionary, and unsuitable groups with 5-year outcomes analyzed. Seven-year outcomes were analyzed for a subset with follow-up of ≥2 years (n=625).Median follow-up was 6.7 years (range, 0.1 to 20.1 y). Patients assigned to cautionary and unsuitable categories were more likely to have high-grade tumors (21% to 25% vs. 9%, P=0.001), receive chemotherapy (15% to 38% vs. 6%, P<0.001), and have close/positive margins (9% to 11% vs. 0%, P<0.001). There was no difference in ipsilateral breast tumor recurrence at 5 or 7 years: 2.2%, 1.2%, 2.8% at 5 years (P=0.57), and 2.2%, 1.9%, 4.6% at 7 years (P=0.58) in the suitable, cautionary, and unsuitable groups, respectively. As compared with the suitable group, increased rates of distant metastases were noted for the unsuitable and cautionary groups at 5 years (P=0.04).No differences in rates of ipsilateral breast tumor recurrence were seen at 5 or 7 years when stratified by ASTRO CS groupings. Modest increases in distant recurrence were noted in the cautionary and unsuitable groups. These findings suggest that the ASTRO CS groupings stratify more for systemic recurrence and may not appropriately select patients for whole versus partial breast irradiation.
Qorvo is a global leader in scalable and dynamic RF solutions for mobile, infrastructure, and defense. To ensure quality and compliance to customer specifications, a large number of product devices and die on wafers are tested every day. Typical data sets will contain unusually large or small values when compared with others in that data set, which may or may not pass specification limits. Such outliers will have negative effects on data analyses such as cpk-analysis, ANOVA, and regressions. More important, outliers, particularly those passing specification limits, may indicate defective parts or pose a reliability concern. For engineering teams in development and production, it is standard practice to identify or label outliers in their data sets, so that they can be removed to improve data analyses and to protect customers against receiving defective parts. The aim for this paper is to provide product engineering teams at Qorvo with reliable outlier labeling methods particularly for skewed distributions. While the ultimate goal is to label and to remove outliers, we also need to balance this with unnecessary yield loss for the financial bottom line.
