Recent Publications by CFE Educators

There is direct evidence of risks at moderate and high levels of radiation dose for highly radiogenic cancers such as leukaemia and thyroid cancer. For many cancer sites, however, it is necessary to assess risks via extrapolation from groups exposed at moderate and high levels of dose, about which there are substantial uncertainties. Crucial to the resolution of this area of uncertainty is the modelling of the dose-response relationship and the importance of both systematic and random dosimetric errors for analyses in the various exposed groups. It is well recognised that measurement error can alter substantially the shape of this relationship and hence the derived population risk estimates. Particular attention has been devoted to the issue of shared errors, common in many datasets, and particularly important in occupational settings. We propose a modification of the regression calibration method which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. This method can be used in settings where there is a mixture of Berkson and classical error. In fits to synthetic datasets in which there is substantial upward curvature in the true dose response, and varying (and sometimes substantial) amounts of classical and Berkson error, we show that the coverage probabilities of all methods for the linear coefficient [Formula: see text] are near the desired level, irrespective of the magnitudes of assumed Berkson and classical error, whether shared or unshared. However, the coverage probabilities for the quadratic coefficient [Formula: see text] are generally too low for the unadjusted and regression calibration methods, particularly for larger magnitudes of the Berkson error, whether this is shared or unshared. In contrast Monte Carlo maximum likelihood yields coverage probabilities for [Formula: see text] that are uniformly too high. The extended regression calibration method yields coverage probabilities that are too low when shared and unshared Berkson errors are both large, although otherwise it performs well, and coverage is generally better than these other three methods. A notable feature is that for all methods apart from extended regression calibration the estimates of the quadratic coefficient [Formula: see text] are substantially upwardly biased.

BACKGROUND

Nipple-sparing mastectomy (NSM) outcomes in the elderly have not been well characterized. The goal of this study was to evaluate NSM outcomes in patients over age 60.

PATIENTS AND METHODS

A single-institution retrospective cohort study was performed for NSM patients over the age of 60 from January 2004 to January 2022. Demographic, intraoperative, and postoperative variables were collected.

RESULTS

We identified 136 women who underwent a total of 200 NSMs at a mean age 65.2 years and with mean body mass index of 25. Most (56%) had invasive breast cancer, requiring neoadjuvant chemotherapy in 15%, and 17.5% had radiation prior to NSM. A total of 91% had immediate tissue expander placement. The infection rate was 19%, with 11.5% requiring expander explantation in the follow-up period. In binomial logistic regression analysis, prior radiation increased the odds of any complication by 2.9 (OR 2.93, CI 1.30-6.58, p = 0.009) and increased the odds of infection by 5.7 (OR 5.70, CI 1.95-16.66, p = 0.001), but no associations were seen for other covariates including age, comorbidities, prior chemotherapy, or presence of invasive disease. Diabetes increased the odds of wound breakdown specifically by 9.0 (OR 8.97, CI 2.01-39.92, p = 0.004). Local recurrence was 3% in mean 3.4-year follow-up.

CONCLUSIONS

Our data support NSM in patients over the age of 60 years with acceptable outcomes within the standard of care. Locoregional recurrence was within the cited range of 0-5%, and only diabetes and prior radiation were associated with reconstructive complications. NSM should thus be offered when appropriate regardless of increased age to achieve oncologic and reconstructive goals.