{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T05:05:38Z","timestamp":1782277538213,"version":"3.54.5"},"reference-count":40,"publisher":"American Society of Clinical Oncology (ASCO)","issue":"5","content-domain":{"domain":["ascopubs.org"],"crossmark-restriction":true},"short-container-title":["JCO Precision Oncology"],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:sec><jats:title>PURPOSE<\/jats:title><jats:p> National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with &gt; 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 &lt; 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. <\/jats:p><\/jats:sec><jats:sec><jats:title>METHODS<\/jats:title><jats:p> An 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). <\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p> Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G<jats:sup>2<\/jats:sup> = 91.3, P &lt; .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have &lt; 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having &lt; 5% or &gt; 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. <\/jats:p><\/jats:sec><jats:sec><jats:title>CONCLUSION<\/jats:title><jats:p> These 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. <\/jats:p><\/jats:sec>","DOI":"10.1200\/po.21.00162","type":"journal-article","created":{"date-parts":[[2021,9,13]],"date-time":"2021-09-13T19:59:00Z","timestamp":1631563140000},"page":"1466-1479","update-policy":"https:\/\/doi.org\/10.1200\/crossmark","source":"Crossref","is-referenced-by-count":42,"title":["Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction"],"prefix":"10.1200","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7529-067X","authenticated-orcid":false,"given":"Eric D.","family":"Whitman","sequence":"first","affiliation":[{"name":"Carol G. 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