ArticleJournal of surgical oncology2022
Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?
Article in Journal of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 6 citations in OpenAlex.
- Survival impact of immediate complete lymph node dissection for Chinese acral and cutaneous melanoma with micrometastasis in sentinel nodes: a retrospective study.Clinical and experimental medicine · 2023Article
- Modern Approach to Melanoma Adjuvant Treatment with Anti-PD1 Immune Check Point Inhibitors orCancers · 2023Article
- Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?Journal of surgical oncology · 2022Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
objectivesOf clinically node-negative (cN0) cutaneous melanoma patients with sentinel lymph node (SLN) metastasis, between 10% and 30% harbor additional metastases in non-sentinel lymph nodes (NSLNs). Approximately 80% of SLN-positive patients have a single positive SLN.
methodsTo assess whether state-of-the-art clinicopathologic models predicting NSLN metastasis had adequate performance, we studied a single-institution cohort of 143 patients with cN0 SLN-positive primary melanoma who underwent subsequent completion lymph node dissection. We used sensitivity (SE) and positive predictive value (PPV) to characterize the ability of the models to identify patients at high risk for NSLN disease.
resultsAcross Stage III patients, all clinicopathologic models tested had comparable performances. The best performing model identified 52% of NSLN-positive patients (SE = 52%, PPV = 37%). However, for the single SLN-positive subgroup (78% of cohort), none of the models identified high-risk patients (SE > 20%, PPV > 20%) irrespective of the chosen probability threshold used to define the binary risk labels. Thus, we designed a new model to identify high-risk patients with a single positive SLN, which achieved a sensitivity of 49% (PPV = 26%).
conclusionFor the largest SLN-positive subgroup, those with a single positive SLN, current model performance is inadequate. New approaches are needed to better estimate nodal disease burden of these patients.
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