ArticleJournal of surgical oncology2025
Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?
Article in Journal of surgical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Comparing the i31-SLNB and the MIA Nomogram for Sentinel Lymph Node Biopsy Positivity Prediction in Cutaneous Melanoma: A Prospective Cohort Analysis.Dermatology and therapy · 2026Article
- Review
- Development of Nomograms to Predict the Probability of Recurrence at Specific Sites in Patients with Cutaneous Melanoma.Cancers · 2025Article
- Surgical Management of Thick Primary Cutaneous Melanoma in the US.Cancer medicine · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
objectivesClinical 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.
methodsWe 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.
resultsOur 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.
conclusionOur 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.
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Registered trials
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