ArticleJournal of the American Academy of Dermatology2023
External validation of the Melanoma Institute Australia Sentinel Node Metastasis Risk Prediction Tool using the National Cancer Database.
Article in Journal of the American Academy of Dermatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- 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
- Validation of risk prediction models for sentinel lymph node metastasis in melanoma in a high UV index region.JAAD international · 2025Article
- Global Applicability of a Risk Prediction Tool for Sentinel Node Positivity in Patients With Primary Cutaneous Melanoma.JAMA dermatology · 2025Article
- Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?Journal of surgical oncology · 2025Article
- Declining Clinical Utility of Tools for Predicting Sentinel Lymph Node Biopsy Status: A Single Institution Experience from 2000 to 2021.Annals of surgical oncology · 2025Article
- Surgical Management of Thick Primary Cutaneous Melanoma in the US.Cancer medicine · 2025Article
- A prospective, multicenter analysis of the integrated 31-gene expression profile test for sentinel lymph node biopsy (i31-GEP for SLNB) test demonstrates reduced number of unnecessary SLNBs in patients with cutaneous melanoma.World journal of surgical oncology · 2025Article
- Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma.Annals of surgical oncology · 2024Article
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5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundTo improve patient selection for sentinel node (SN) biopsy, the Melanoma Institute of Australia (MIA) created a predictive model based on readily available clinicopathologic factors.
objectivesValidation of the MIA nomogram using the National Cancer Database (NCDB), a nationwide oncology outcomes database for >1500 Commission-accredited cancer programs in the United States.
methodsA total of 60,165 patients were included in the validation. The probability of SN positivity was calculated for each patient. Using calculated probabilities, a receiver operating characteristic curve was generated to assess the model's discrimination ability.
resultsAt baseline, the NCDB cohort had different clinicopathologic characteristics compared with the original MIA data set. Despite these differences, the MIA nomogram retained high-predictive accuracy within the NCDB dataset (C-statistic, 0.733 [95% CI, 0.726-0.739]), although calibration weakened for the highest risk decile. LIMITATIONS: The NCDB collects data from hospital registries accredited by the Commission on Cancer.
conclusionsIn conclusion, this study validated the use of the MIA nomogram in a nationwide oncology outcomes database collected from >1500 Commission-accredited cancer programs in the United States, demonstrating the potential for this nomogram to predict SN positivity and reduce the number of negative SN biopsies.
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