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We aimed to develop methods to better differentiate patients whose survival was &gt;90\u2009days.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We evaluated 83 characteristics of 106 treatment\u2010na\u00efve, stage IV NSCLC patients with Eastern Cooperative Oncology Group Performance Status (ECOG\u2010PS) &gt;1. Automated machine learning was used to select a model and optimize hyperparameters. 100\u2010fold bootstrapping was performed for dimensionality reduction for a second (\u201clite\u201d) model. Performance was measured by C\u2010statistic and accuracy metrics in an out\u2010of\u2010sample validation cohort. The \u201clite\u201d model was validated on a second independent, prospective cohort (<jats:italic>N<\/jats:italic>\u00a0=\u00a042). Network analysis (NA) was performed to evaluate the differences in centrality and connectivity of features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The selected method was ExtraTrees Classifier, with C\u2010statistic of 0.82 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01) and accuracy of 0.81 (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.01). The \u201clite\u201d model had 16 variables and obtained C\u2010statistic of 0.84 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01) and accuracy of 0.75 (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.039) in the first cohort, and C\u2010statistic of 0.706 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01) and accuracy of 0.714 (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.01) in the second cohort. The networks of patients with lower survival were more interconnected. Features related to cachexia, inflammation, and quality of life had statistically different prestige scores in NA.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine learning can assist in the prognostic evaluation of advanced NSCLC. The model generated with a reduced number of features showed high accessibility and reasonable metrics. Features related to quality of life, cachexia, and performance status had increased correlation and importance scores, suggesting that they play a role at later disease stages, in line with the biological rationale already described.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.5254","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T07:20:41Z","timestamp":1664176841000},"page":"5099-5109","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting survival in metastatic non\u2010small cell lung cancer patients with poor ECOG\u2010PS: A single\u2010arm prospective study"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9101-8553","authenticated-orcid":false,"given":"Mateus Trinconi","family":"Cunha","sequence":"first","affiliation":[{"name":"Servi\u00e7o de Oncologia Cl\u00ednica, Instituto do C\u00e2ncer do Estado de S\u00e3o Paulo, Hospital das 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