Integrating 31-gene expression profiling with clinicopathologic features to optimize cutaneous melanoma sentinel lymph node metastasis prediction

Eric D. Whitman, Vadim P. Koshenkov, Brian R. Gastman, Deri Lewis, Eddy C. Hsueh, Ho Pak, Thomas P. Trezona, Robert S. Davidson, Michael McPhee, J. Michael Guenther, Paul Toomey, Franz O. Smith, Peter D. Beitsch, James M. Lewis, Andrew Ward, Shawn E. Young, Parth K. Shah, Ann P. Quick, Brian J. Martin, Olga ZolochevskaKyle R. Covington, Federico A. Monzon, Matthew S. Goldberg, Robert W. Cook, Martin D. Fleming, David M. Hyams, John T. Vetto

    Research output: Contribution to journalArticlepeer-review

    Abstract

    PURPOSE 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. METHODS 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). RESULTS Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P, .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, 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, 5% or . 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. CONCLUSION 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.

    Original languageEnglish (US)
    Pages (from-to)1466-1479
    Number of pages14
    JournalJCO Precision Oncology
    Volume5
    DOIs
    StatePublished - 2021

    ASJC Scopus subject areas

    • Oncology
    • Cancer Research

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