ArticleJCO precision oncology2021
Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction.
Article in JCO precision oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 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
- The integrated 31-gene expression profile test identifies low-risk patients with cutaneous melanoma who can forego the SLNB procedure: results from a prospective, multicenter trial.Future oncology (London, England) · 2026Article
- Flawed Benchmarks and Biased Cohorts: Reassessing the Evidence for CP-GEP (Merlin) Versus 31-GEP (DecisionDx-Melanoma).International journal of dermatology · 2026Article
- 31-Gene expression profiling for cutaneous melanoma: an expert consensus panel.Dermatology online journal · 2025Review
- Review
- Validation of a Clinicopathologic and Gene Expression Model for Predicting Sentinel Node Metastasis in Melanoma: A Multicenter Danish Cohort Study.Journal of surgical oncology · 2025Article
- Validation of Invasive Area for Predicting Sentinel Node Status and Survival in Primary Cutaneous Melanoma.Annals of surgical oncology · 2025Article
- Validation of risk prediction models for sentinel lymph node metastasis in melanoma in a high UV index region.JAAD international · 2025Article
- Cutaneous melanoma.Nature reviews. Disease primers · 2025Review
- Society of Surgical Oncology Consensus Statement: Assessing the Evidence for and Utility of Gene Expression Profiling of Primary Cutaneous Melanoma.Annals of surgical oncology · 2025Article
- Are Nomograms Useful for Predicting Sentinel Lymph Node Status in Melanoma Patients?Journal of surgical oncology · 2025Article
- Prognostic Biomarkers in Evolving Melanoma Immunotherapy.American journal of clinical dermatology · 2025Review
- 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
- 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
- The Prognostic Value of the 31-Gene Expression Profile Test in Cutaneous Melanoma: A Systematic Review and Meta-Analysis.Cancers · 2024Review
- Review
- Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide personalized precision estimates for sentinel lymph node positivity: an independent performance cohort.World journal of surgical oncology · 2024Article
- Management of melanoma: can we use gene expression profiling to help guide treatment and surveillance?Clinical & experimental metastasis · 2024Review
- Validation of the Melanoma Institute of Australia's Sentinel Lymph Node Biopsy Risk Prediction Tool for Cutaneous Melanoma.Annals of surgical oncology · 2024Article
Corrections and comments
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Authors and funding
27 authors.
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No grant is acknowledged in the PubMed record.
Abstract
National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction.
methodsAn integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674).
resultsCompared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G
conclusionThese data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.
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