{
  "abstract": "Accurate prediction of breast cancer recurrence remains difficult because prognosis varies significantly across molecular subtypes. This underscores the need for scalable, affordable, and interpretable prognostic tools. We developed machine learning models integrating hematological indices with clinicopathologic data to predict 2- and 10-year recurrence or all-case death. We retrospectively analyzed 4277 women with primary breast cancer (2008–2022). The cohort included hormone receptor-positive (HR + ), HER2-positive, and triple-negative (TNBC) subtypes. We trained multiple classifiers and integrated them into a stacked ensemble using logistic regression as the final learner. Class imbalance was addressed with SMOTE applied to training sets. The ensemble achieved strong discrimination: general cohort AUC 0.859 (2-year) and 0.811 (10-year), with specificity 84–82% and sensitivity 68–61%. Subtype-specific performance remained robust across time horizons: HR + AUC 0.862/0.804, HER2 + AUC 0.877/0.831, and TNBC AUC 0.826/0.826 (2-year/10-year respectively). SHAP analysis identified advanced tumor stage, elevated inflammatory ratios (NLR, PLR, MLR), elevated red cell distribution width, and age as key adverse predictors with stronger effects on early recurrence. This interpretable tool uses routine blood tests and clinico-pathological data at diagnosis. It flaggs high-risk patients for intensified therapy, useful where genomic testing is unavailable.",
  "abstract_shown": true,
  "affiliations": [
    "Instituto de Tecnologia e Liderança, São Paulo, SP Brazil",
    "Center for Translational Research in Oncology (LIM24), Instituto do Câncer do Estado de São Paulo, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil",
    "Comprehensive Center for Precision Oncology, Universidade de São Paulo, São Paulo, Brazil",
    "Serviço de Hematologia, Hemoterapia e Terapia Celular and Laboratório de Investigação Médica (LIM) 31, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil",
    "Instituto do Câncer do Estado de São Paulo, São Paulo, SP Brazil",
    "Instituto D’Or de Pesquisa e Ensino (IDOR), São Paulo, SP Brazil",
    "Faculdade de Informática e Administração Paulista, São Paulo, SP Brazil",
    "Escola de Artes, Ciências e Humanidades, Universidade de São Paulo, São Paulo, Brazil"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Patricia Honorato Moreira",
      "orcid": "0009-0008-5667-0675"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Arthur Shuzo Owtake Cardoso",
      "orcid": "0000-0002-7943-9715"
    },
    {
      "affs": [
        3
      ],
      "name": "Rafael de Oliveira",
      "orcid": "0009-0001-3425-0273"
    },
    {
      "affs": [
        3
      ],
      "name": "Joaquim Gasparini dos Santos",
      "orcid": "0000-0001-6096-116X"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Renata Colombo Bonadio",
      "orcid": "0000-0001-5818-922X"
    },
    {
      "affs": [
        4,
        5
      ],
      "name": "Wesley Antonio Lopes de Lima",
      "orcid": "0009-0009-3796-232X"
    },
    {
      "affs": [
        6
      ],
      "name": "Thomas de Almeida Reichmann",
      "orcid": "0009-0000-7806-4117"
    },
    {
      "affs": [
        4
      ],
      "name": "Bruna Salani Mota",
      "orcid": "0000-0001-9567-1066"
    },
    {
      "affs": [
        2,
        7
      ],
      "name": "Alexandre Ferreira Ramos",
      "orcid": "0000-0003-4681-3069"
    },
    {
      "affs": [
        0
      ],
      "name": "Flavia Santoro",
      "orcid": "0000-0003-3421-1984"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Roger Chammas",
      "orcid": "0000-0003-0342-8726"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Luciana Rodrigues Carvalho Barros",
      "orcid": "0000-0002-8633-716X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cz8ka7640b83a.bib",
  "cited_by": 0,
  "doi": "10.1038/s41523-026-01005-z",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13736949/",
  "id": "cz8ka7640b83a",
  "issns": [
    "2374-4677"
  ],
  "landing_url": "https://doi.org/10.1038/s41523-026-01005-z",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1306|Breast Cancer Treatment Studies"
    ]
  ],
  "openalex": "W7168016498",
  "pdf_url": "https://www.nature.com/articles/s41523-026-01005-z_reference.pdf",
  "pmcid": "PMC13736949",
  "pmid": "42436188",
  "published": "2026-07-11",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/cz8ka7640b83a.ris",
  "sources": [
    {
      "first_seen": "2026-11-09T12:22:17Z",
      "native_id": "PMC13736949",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41523-026-01005-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:1306|Breast Cancer Treatment Studies",
      "subfield": "oncology"
    }
  ],
  "title": "Ensemble machine learning for predicting breast cancer recurrence and mortality using clinical and hemogram data",
  "title_text": "Ensemble machine learning for predicting breast cancer recurrence and mortality using clinical and hemogram data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T12:22:17Z",
  "url": "https://research.draconitic.com/paper/cz8ka7640b83a",
  "venue": "NPJ Breast Cancer",
  "versions": []
}