Body image dissatisfaction in patients undergoing breast reconstruction: Examining the roles of breast symmetry and appearance investment.

OBJECTIVE: Reconstruction as part of treatment for breast cancer is aimed at mitigating body image concerns after mastectomy. Although algorithms have been developed to objectively assess breast reconstruction outcomes, associations between objectively quantified breast aesthetic appearance and patient-reported body image outcomes have not been examined. Further, the role of appearance investment in explaining a patient's body image is not well understood. We investigated the extent to which objectively quantified breast symmetry and patient-reported appearance investment were associated with body image dissatisfaction in patients undergoing cancer-related breast reconstruction. METHODS: Breast cancer patients in different stages of reconstruction (n = 190) completed self-report measures of appearance investment and body image dissatisfaction. Vertical extent and horizontal extent symmetry values, which are indicators of breast symmetry, were calculated from clinical photographs. Associations among breast symmetry, appearance investment, body image dissatisfaction, and patient clinical factors were examined. Multi-variable regression was used to evaluate the extent to which symmetry and appearance investment were associated with body image dissatisfaction. RESULTS: Vertical extent symmetry, but not horizontal extent symmetry, was associated with body image dissatisfaction. Decreased vertical extent symmetry (ß = -.19, P < .05) and increased appearance investment (ß = .45, P < .001) were significantly associated with greater body image dissatisfaction while controlling for clinical factors. CONCLUSIONS: Breast symmetry and patient appearance investment both significantly contribute to an understanding of patient-reported body image satisfaction during breast reconstruction treatment.

UT Biomedical Informatics Lab (BMIL) Probability Wheel.

A probability wheel app is intended to facilitate communication between two people, an "investigator" and a "participant," about uncertainties inherent in decision-making. Traditionally, a probability wheel is a mechanical prop with two colored slices. A user adjusts the sizes of the slices to indicate the relative value of the probabilities assigned to them. A probability wheel can improve the adjustment process and attenuate the effect of anchoring bias when it is used to estimate or communicate probabilities of outcomes. The goal of this work was to develop a mobile application of the probability wheel that is portable, easily available, and more versatile. We provide a motivating example from medical decision-making, but the tool is widely applicable for researchers in the decision sciences.

Helping patients make choices about breast reconstruction: a decision analysis approach.

Decision analysis can help breast reconstruction patients and their surgeons to methodically evaluate clinical alternatives and make hard decisions. The purpose of this article is to help plastic surgeons guide patients in making decisions though a case study in breast reconstruction. By making good decisions, patient outcomes may be improved. This article aims to illustrate decision analysis techniques from the patient perspective, with an emphasis on her values and preferences. The authors introduce normative decision-making through a fictional breast reconstruction patient and systematically build the decision basis to help her make a good decision. The authors broadly identify alternatives of breast reconstruction, propose types of outcomes that the patient should consider, discuss sources of probabilistic information and outcome values, and demonstrate how to make a good decision. The concepts presented here may be extended to other shared decision-making problems in plastic and reconstructive surgery. In addition, the authors discuss how sensitivity analysis may test the robustness of the decision and how to evaluate the quality of decisions. The authors also present tools to help implement these concepts in practice. Finally, the authors examine limitations that hamper adoption of patient decision analysis in reconstructive surgery and health care in general. In particular, the authors emphasize the need for routine collection of quality-of-life information, out-of-pocket expense, and recovery time.

Assessing Women's Preferences and Preference Modeling for Breast Reconstruction Decision-Making.

BACKGROUND: Women considering breast reconstruction must make challenging trade-offs amongst issues that often conflict. It may be useful to quantify possible outcomes using a single summary measure to aid a breast cancer patient in choosing a form of breast reconstruction. METHODS: In this study, we used multiattribute utility theory to combine multiple objectives to yield a summary value using nine different preference models. We elicited the preferences of 36 women, aged 32 or older with no history of breast cancer, for the patient-reported outcome measures of breast satisfaction, psychosocial well-being, chest well-being, abdominal well-being, and sexual wellbeing as measured by the BREAST-Q in addition to time lost to reconstruction and out-of-pocket cost. Participants ranked hypothetical breast reconstruction outcomes. We examined each multiattribute utility preference model and assessed how often each model agreed with participants' rankings. RESULTS: The median amount of time required to assess preferences was 34 minutes. Agreement among the nine preference models with the participants ranged from 75.9% to 78.9%. None of the preference models performed significantly worse than the best performing risk averse multiplicative model. We hypothesize an average theoretical agreement of 94.6% for this model if participant error is included. There was a statistically significant positive correlation with more unequal distribution of weight given to the seven attributes. CONCLUSIONS: We recommend the risk averse multiplicative model for modeling the preferences of patients considering different forms of breast reconstruction because it agreed most often with the participants in this study.

Plastic Surgeon Expertise in Predicting Breast Reconstruction Outcomes for Patient Decision Analysis.

BACKGROUND: Decision analysis offers a framework that may help breast cancer patients make good breast reconstruction decisions. A requirement for this type of analysis is information about the possibility of outcomes occurring in the form of probabilities. The purpose of this study was to determine if plastic surgeons are good sources of probability information, both individually and as a group, when data are limited. METHODS: Seven plastic surgeons were provided with pertinent medical information and preoperative photographs of patients, and were asked to assign probabilities to predict number of revisions, complications, and final aesthetic outcome using a questionnaire designed for the study. Logarithmic strictly proper scoring was used to evaluate the surgeons' abilities to predict breast reconstruction outcomes. Surgeons' responses were analyzed for calibration and confidence in their answers. RESULTS: As individuals, there was variation in surgeons' ability to predict outcomes. For each prediction category, a different surgeon was more accurate. As a group, surgeons possessed knowledge of future events despite not being well calibrated in their probability assessments. Prediction accuracy for the group was up to six-fold greater than that of the best individual. CONCLUSIONS: The use of individual plastic surgeon-elicited probability information is not encouraged unless the individual's prediction skill has been evaluated. In the absence of this information, a group consensus on the probability of outcomes is preferred. Without a large evidence base for calculating probabilities, estimates assessed from a group of plastic surgeons may be acceptable for purposes of breast reconstruction decision analysis.

Recent advances in computational analysis of mass spectrometry for proteomic profiling.

The proteome, defined as an organism's proteins and their actions, is a highly complex end-effector of molecular and cellular events. Differing amounts of proteins in a sample can be indicators of an individual's health status; thus, it is valuable to identify key proteins that serve as 'biomarkers' for diseases. Since the proteome cannot be simply inferred from the genome due to pre- and posttranslational modifications, a direct approach toward mapping the proteome must be taken. The difficulty in evaluating a large number of individual proteins has been eased with the development of high-throughput methods based on mass spectrometry (MS) of peptide or protein mixtures, bypassing the time-consuming, laborious process of protein purification. However, proteomic profiling by MS requires extensive computational analysis. This article describes key issues and recent advances in computational analysis of mass spectra for biomarker identification.

Activation of Go-coupled dopamine D2 receptors inhibits ERK1/ERK2 in pituitary cells. A key step in the transcriptional suppression of the prolactin gene.

In pituitary lactotrophs the prolactin gene is stimulated by neuropeptides and estrogen and is suppressed by dopamine via D2-type receptors. Stimulatory signals converge on activation of the mitogen-activated protein kinases ERK1/2, but dopamine regulation of this pathway is not well defined. Paradoxically, D2 agonists activate ERK1/2 in many cell types. Here we show that in prolactin-secreting GH4ZR7 cells and primary pituitary cells, dopamine treatment leads to a rapid, pronounced, and specific decrease in activated ERK1/2. The response is blocked by D2-specific antagonists and pertussis toxin. Interestingly, in stable lines expressing specific pertussis toxin-resistant Galpha subunits, toxin treatment blocks dopamine suppression of MAPK in Galpha(i2)- but not Galphao-expressing cells, demonstrating that G(o)-dependent pathways can effect the inhibitory MAPK response. At the nuclear level, the MEK1 inhibitor U0126 mimics the D2-agonist bromocryptine in suppressing levels of endogenous prolactin transcripts. Moreover, a good correlation is seen between the IC(50) values for inhibition of MEK1 and suppression of prolactin promoter function (PD184352 > U0126 > U0125). Both dopamine and U0126 enhance the nuclear localization of ERF, a MAPK-sensitive ETS repressor that inhibits prolactin promoter activity. In addition, U0126 suppression is transferred by tandem copies of the Pit-1-binding site, consistent with mapping experiments for dopamine responsiveness. Our data suggest that ERK1/2 suppression is an obligatory step in the dopaminergic control of prolactin gene transcription and that bidirectional control of ERK1/2 function in the pituitary may provide a key mechanism for endocrine gene control.