{
  "abstract": "Introduction Lung cancer remains the leading cause of cancer mortality worldwide despite advances in treatment. Patient-related factors beyond tumour characteristics may influence prognosis but are seldom integrated into staging or risk stratification.Methods We analysed data from 59 101 patients with resectable non-small cell lung cancer (NSCLC) in the French nationwide Epithor database. Using an unsupervised machine learning approach, we applied principal component analysis followed by k-means clustering on 13 patient-specific clinical variables excluding tumour or treatment features, to identify distinct patient phenotypes. Survival differences were evaluated with Kaplan-Meier analysis and Cox proportional hazards models.Results Five distinct patient phenotypes emerged, reflecting differing distributions of sex, age, pulmonary function, comorbidity and performance status. These phenotypes also showed varied distributions of pathological stage and histology, confirming their clinical relevance. Survival outcomes differed significantly between clusters, with 5-year overall survival ranging from 76.6% (95% CI 75.8% to 77.3%) to 53.8% (95% CI 52.9% to 54.6%). Using the lowest-risk cluster (Cluster 0) as reference, crude HRs for mortality were significantly different between clusters: Cluster 1 (HR 1.48 (95% CI 1.42 to 1.54)), Cluster 2 (HR 1.94 (95% CI 1.84 to 2.03)), Cluster 3 (HR 2.45 (95% CI 2.36 to 2.55)) and Cluster 4 (HR 1.95 (95% CI 1.87 to 2.04)). Cluster assignment remained an independent predictor of survival after adjustment for usual confounders (p<0.001).Conclusions Phenotype-based clustering of non-tumour patient characteristics identifies clinically meaningful subgroups with distinct survival patterns after NSCLC surgery. Incorporating these phenotypes into preoperative evaluation may enhance risk stratification and support personalised treatment strategies alongside traditional tumour staging.",
  "authors": [
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Cochin, Paris, France"
      ],
      "name": "Lorenzo Gherzi"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Cochin, Paris, France"
      ],
      "name": "Elisa Daffré"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Cochin, Paris, France"
      ],
      "name": "Mathilde Prieto"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Nord, Marseille, France"
      ],
      "name": "Pascal Alexandre Thomas"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Européen Georges Pompidou, Paris, France"
      ],
      "name": "Françoise Le Pimpec-Barthes"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Les Hôpitaux Universitaires de Strasbourg, Strasbourg, France"
      ],
      "name": "Pierre-Emmanuel Falcoz"
    },
    {
      "affiliations": [
        "Thoracic surgery Department, Hôpital Larrey, Toulouse, France"
      ],
      "name": "Laurent Brouchet"
    },
    {
      "affiliations": [
        "Thoracic surgery Department, Hôpital Larrey, Toulouse, France"
      ],
      "name": "Marcel Dahan"
    },
    {
      "affiliations": [
        "Thoracic Surgery Department, Hôpital Cochin, Paris, France"
      ],
      "name": "Marco Alifano"
    }
  ],
  "title": "Clustering by patients-related characteristics identifies specific phenotypes with different prognosis after resection of non-small cell lung cancer: a French nationwide study from the Epithor database",
  "uid": "d8c3b9bb-5ce4-5e8c-a88f-88984ed65a64"
}