An external validation of a novel predictive algorithm for male nipple areolar positioning: an improvement to current practice through a multicenter endeavor.
Floyd W TimmermansLaure RuyssinckSterre E MokkenMarlon BuncamperKevin M VeenMargriet G MullenderKarel E Y ClaesMark-Bram BoumanStanislas MonstreyTimotheus C van de GriftPublished in: Journal of plastic surgery and hand surgery (2021)
The correct positioning of nipple-areolar complexes (NAC) during gender-affirming mastectomies remains a particular challenge. Recently, a Dutch two-step algorithm was proposed predicting the most ideal NAC-position derived from a large cisgender male cohort. We aimed to externally validate this algorithm in a Belgian cohort. The Belgian validation cohort consisted of cisgender men. Based on patient-specific anthropometry, the algorithm predicts nipple-nipple distance (NN) and sternal-notch-to-nipple distance (SNN). Predictions were externally validated using the performance measures: R2-value, means squared error (MSE) and mean absolute percentage error (MAPE). Additionally, data were collected from a Belgian and Dutch cohort of transgender men having undergone mastectomy with free nipple grafts. The observed and predicted NN and SNN were compared and the inter-center variability was assessed. A total of 51 Belgian cisgender and 25 transgender men were included, as well as 150 Dutch cisgender and 96 transgender men. Respectively, the performance measures (R2-value, MSE and MAPE) for NN were 0.315, 2.35 (95%CI:0-6.9), 4.9% (95%CI:3.8-6.1) and 0.423, 1.51 (95%CI:0-4.02), 4.73%(95%CI:3.7-5.7) for SNN. When applying the algorithm to both transgender cohorts, the predicted SNN was larger in both Dutch (17.1measured(±1.7) vs. 18.7predicted(±1.4), p= <0.001) and Belgian (16.2measured(±1.8) vs. 18.4predicted(±1.5), p= <0.001) cohorts, whereas NN was too long in the Belgian (22.0measured(±2.6) vs. 21.2predicted(±1.6), p = 0.025) and too short in the Dutch cohort (19.8measured(±1.8) vs. 20.7predicted(±1.9), p = 0.001). Both models performed well in external validation. This indicates that this two-step algorithm provides a reproducible and accurate clinical tool in determining the most ideal patient-tailored NAC-position in transgender men seeking gender-affirming chest surgery.
Keyphrases
- breast reconstruction
- machine learning
- deep learning
- middle aged
- hiv testing
- transcription factor
- neural network
- healthcare
- big data
- minimally invasive
- cell proliferation
- primary care
- men who have sex with men
- artificial intelligence
- case report
- hepatitis c virus
- acute coronary syndrome
- high resolution
- double blind
- genome wide analysis