ArticleJournal of patient-reported outcomes2024
Anthropometric estimates can predict satisfaction with breast in a population of asymptomatic women.
Article in Journal of patient-reported outcomes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
backgroundSeveral authors hypothesized that normative values of breast related quality of life in asymptomatic populations can be helpful to better understand changes induced by surgery. Breast related quality of life can be associated to breast anthropometry. This study was designed to explore this hypothesis, find relevant correlations and, using machine learning techniques, predict values of satisfaction with breast from easy body measurements.
methodsAsymptomatic women undergoing routine clinical examination for breast cancer prevention were interviewed using the BREAST_Q V1 Breast Conserving Surgery Pre-op. Descriptive statistics was performed to describe the characteristics of the population. The Pearson correlation test defined correlation between relevant anthropometric variables and scores in each domain of the BREAST_Q. Regression analysis was employed to assess variation in the "Satisfaction with breast" domain when looking at the mirror dressed or undressed. Three machine learning algorithms were tested to predict scores in the "Satisfaction with breast domain" given body mass index and nipple to sternal notch distance.
resultsOne-hundred and twenty-five women underwent clinical examination and assessment of anthropometry. The reply rate to the BREAST_Q ranged from 99.2 to 88% depending on the domains. The "satisfaction with breast" domain was negatively associated either to BMI [r
conclusionsThis study generates normative scores for a Mediterranean population of asymptomatic women and demonstrates relevant associations between anthropometry and breast related quality of life. Machine learning techniques may predict scores of the "satisfaction with breast" domain of the Breast_Q using body mass index and nipple to sternal notch estimates as input. However, the algorithm seems to fail in approximately one third of the sample probably because is not able to capture many aspects of personal life. Much larger sample and more qualitative research is required before establishing any direct association between body estimates and quality of life. Clinical implications are given.
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