Artificial intelligence estimates the importance of baseline factors in predicting response to anti-PD1 in metastatic melanoma

Alice Indini, Lorenza Di Guardo, Carolina Cimminiello, Filippo De Braud, Michele Del Vecchio

Research output: Contribution to journalArticle

Abstract

Objective: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is 40% and baseline biomarkers for the outcome are yet to be identified. Here, we aimed to determine whether artificial intelligence might be useful in weighting the importance of baseline variables in predicting response to anti-PD1. Methods: This is a retrospective study evaluating 173 patients receiving anti-PD1 for melanoma. Using an artificial neuronal network analysis, the importance of different variables was estimated and used in predicting response rate and overall survival. Results: After a mean follow-up of 12.8 (±11.9) months, disease control rate was 51%. Using artificial neuronal network, we observed that 3 factors predicted response to anti-PD1: neutrophil-to-lymphocyte ratio (NLR) (importance: 0.195), presence of ≥3 metastatic sites (importance: 0.156), and baseline lactate dehydrogenase (LDH) > upper limit of normal (importance: 0.154). Looking at connections between different covariates and overall survival, the most important variables influencing survival were: presence of ≥3 metastatic sites (importance: 0.202), age (importance: 0.189), NLR (importance: 0.164), site of primary melanoma (cutaneous vs. noncutaneous) (importance: 0.112), and LDH > upper limit of normal (importance: 0.108). Conclusions: NLR, presence of ≥3 metastatic sites, LDH levels, age, and site of primary melanoma are important baseline factors influencing response and survival. Further studies are warranted to estimate a model to drive the choice to administered anti-PD1 treatments in patients with melanoma.

Original languageEnglish
Pages (from-to)643-648
Number of pages6
JournalAmerican Journal of Clinical Oncology: Cancer Clinical Trials
Volume42
Issue number8
DOIs
Publication statusPublished - Aug 1 2019

Keywords

  • anti-PD1
  • artificial intelligence
  • artificial neural network
  • metastatic melanoma
  • response

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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