Breast symmetry and patient appearance investment both significantly contribute to an understanding of patient-reported body image satisfaction during breast reconstruction treatment.

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.

Background:Women considering breast reconstruction must make challenging trade-offs among 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 9 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 well-being 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 9 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 7 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.

Decision analysis can help breast reconstruction patients and their surgeons to methodically evaluate clinical alternatives and make hard decisions. The purpose of this paper 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 paper aims to illustrate decision analysis techniques from the patient perspective with an emphasis on her values and preferences. We introduce normative decision-making through a fictional breast reconstruction patient and systematically build the decision basis to help her make a good decision. We 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, we discuss how sensitivity analysis may test the robustness of the decision and how to evaluate the quality of decisions. We also present tools to help implement these concepts in practice. Finally, we examine limitations that hamper adoption of patient decision analysis in reconstructive surgery and healthcare in general. In particular, we emphasize the need for routine collection of quality of life information, out-of-pocket expense, and recovery time.