{
  "abstract": "SUMMARY: OBJECTIVE:: The aim of this study was to develop and validate machine learning models integrating clinical and pre-treatment dynamic contrast-enhanced magnetic resonance imaging radiomic features for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer.\n\nMETHODS:: This retrospective study analyzed 299 breast cancer patients from the publicly available Duke Breast Cancer magnetic resonance imaging dataset who received neoadjuvant chemotherapy. From pre-treatment dynamic contrast-enhanced magnetic resonance imaging, 534 IBSI-compliant radiomic features were extracted. A five-method consensus feature selection pipeline retained 24 features. Notably, three prediction models were developed: clinical-only (Model A), radiomic-only (Model B), and combined clinical–radiomic (Model C). In total, seven machine learning algorithms were compared using 20 repeated five-fold stratified cross-validation with SMOTE applied within each training fold and assessed on a held-out test set (30%, n=90). Performance was evaluated using AUC with bootstrap confidence intervals, calibration (Brier score), and decision curve analysis.\n\nRESULTS:: The pathologic complete response rate was 20.4% (61/299). The combined model (Gradient Boosting) achieved the highest test AUC of 0.723 (95%CI 0.595–0.835), with sensitivity of 0.333 and specificity of 0.833. DeLong tests revealed no statistically significant differences between model pairs (all p>0.05). Decision curve analysis demonstrated the highest net benefit for the combined model across 10–50% threshold probabilities.\n\nCONCLUSION:: The combined clinical–radiomic model demonstrated modest discriminative ability for pathologic complete response prediction. The limited sensitivity suggests potential utility as a rule-out test to identify patients unlikely to achieve pathologic complete response. These hypothesis-generating findings require external validation in independent multi-center cohorts.",
  "abstract_shown": true,
  "affiliations": [
    "Afyonkarahisar University of Health Sciences, Faculty of Medicine, Department of Radiology – Afyonkarahisar, Turkey."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Emin Demirel",
      "orcid": "0000-0002-0675-3893"
    },
    {
      "affs": [
        0
      ],
      "name": "Huseyin Coskun",
      "orcid": "0000-0001-5405-3544"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7hdk8zshjfbkg.bib",
  "cited_by": 0,
  "doi": "10.1590/1806-9282.20260878",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13733276/",
  "id": "7hdk8zshjfbkg",
  "issns": [
    "0104-4230",
    "1806-9282"
  ],
  "landing_url": "https://doi.org/10.1590/1806-9282.20260878",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Original Article"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7225082340",
  "pdf_url": "https://www.scielo.br/j/ramb/a/sXyKMz3BFNHywggbBPbXvfy/?lang=en&format=pdf",
  "pmcid": "PMC13733276",
  "pmid": "42987080",
  "published": "2026-11-02",
  "publisher": "Associação Médica Brasileira",
  "ris": "https://research.draconitic.com/paper/7hdk8zshjfbkg.ris",
  "sources": [
    {
      "first_seen": "2026-11-08T12:23:06Z",
      "native_id": "PMC13733276",
      "source": "pmc",
      "url": "https://doi.org/10.1590/1806-9282.20260878"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer",
  "title_text": "Magnetic resonance imaging radiomics for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T12:23:06Z",
  "url": "https://research.draconitic.com/paper/7hdk8zshjfbkg",
  "venue": "Revista da Associação Médica Brasileira",
  "versions": []
}