Journal of surgical oncology · 2024
Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Lourdault Kristel, Cowman Arthur W, Hanes Douglas, Scholer Anthony J, Aguilar Tyler, Essner Richard
Аннотация
BACKGROUND AND OBJECTIVES: Clinical nomograms have been developed to predict sentinel lymph node (SLN) status in early-stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well-validated Melanoma Institute Australia (MIA) nomogram.
METHODS: We developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial-I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.
RESULTS: Our model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661-0.751). Our model achieved an AUC of 0.715 (0.706-0.724) compared to 0.723 (0.715-0.731) with the MIA model, using the NCDB set.
CONCLUSION: Our model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.
METHODS: We developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial-I. The model was externally validated using the National Cancer Database (NCDB) data set, and its performance was compared to that of the MIA nomogram.
RESULTS: Our model had good discrimination between positive and negative SLNs, with a training set area under the curve (AUC) of 0.706 (0.661-0.751). Our model achieved an AUC of 0.715 (0.706-0.724) compared to 0.723 (0.715-0.731) with the MIA model, using the NCDB set.
CONCLUSION: Our model performed similarly to the MIA model, confirming that despite using different clinical features and data sets, no clinical nomogram is currently accurate enough for clinical use.