Evidence map›Paper›PMID 39215342›Full record

ArticleWorld journal of surgical oncology2024

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.

Chase Kriza, Brian Martin, Christine N Bailey, Joseph Bennett

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Article in World journal of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Chase KrizaChristianaCare Helen F. Graham Cancer Center & Research Institute, Newark, DE, USA.
Brian MartinCastle Biosciences, Friendswood, TX, USA.
Christine N BaileyCastle Biosciences, Friendswood, TX, USA.
Joseph BennettChristianaCare Helen F. Graham Cancer Center & Research Institute, Newark, DE, USA. jbennett5@lifebridgehealth.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUp to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1 A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated 31-GEP (i31-GEP) combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.

methodsPatients from a single surgical center with 31-GEP results were included (n = 156). An i31-GEP risk prediction < 5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.

resultsPatients considered low risk by the i31-GEP had a 0% (0/30) SLN positivity rate compared to a 31.9% (30/94, p < 0.001) positivity rate in those with > 10% risk. Using the i31-GEP to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p < 0.001) for all patients and 33.0% (30/91, p < 0.001) for T1-T2 tumors. Patients with T1-T2 tumors and an i31-GEP-predicted SLN positivity risk > 10% had a similar SLN positivity rate (33.3%) as patients with T3-T4 tumors (31.3%).

conclusionThe i31-GEP identified patients with < 5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.

Indexed as

Gene Expression ProfilingLymphatic MetastasisMelanomaSentinel Lymph NodeSentinel Lymph Node BiopsyAdultAgedAged, 80 and overBiomarkers, TumorFemaleFollow-Up StudiesHumansMaleMiddle AgedPrecision MedicinePrognosisBiomarkers, Tumor31-GEPCutaneous melanomaGene expression profilingPrognosisSentinel lymph node biopsy

Identifiers

PMID39215342
PMCPMC11363455

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.