{
  "abstract": "Background This study aimed to evaluate the ability of mammography-based radiomics combined with machine learning to predict pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NACT), addressing the limited research on mammography radiomics. Methods A retrospective cohort of 408 female breast cancer patients who underwent neoadjuvant chemotherapy (NACT) followed by surgery from January 2022 to December 2023 was analyzed. Breast-only pCR was defined as the absence of residual invasive carcinoma in the breast tumor bed and did not incorporate axillary lymph node status. Delta radiomics features, representing quantitative changes from pre- and post-NACT mammography images, were extracted. After evaluating 18 machine learning algorithms using cross-validation, CatBoost, ExtraTrees, and Logistic Regression were selected, optimized, and validated independently. A stacking ensemble was also constructed. Model performance was assessed using AUC, accuracy, and precision, with DeLong’s test for statistical comparisons. Results Among the 408 patients, 87 (21.32%) achieved pCR. On the independent test set, ExtraTrees, CatBoost, and Logistic Regression models achieved AUCs of 0.854 (95% CI 0.773–0.936), 0.836 (95% CI 0.745–0.926), and 0.775 (95% CI 0.683–0.867), respectively. The stacking ensemble model achieved the numerically highest AUC of 0.884 (95% CI 0.807–0.961). HER2 status, hormone receptor status, and some specific radiomic features were consistently identified as important contributors to pCR assessment. DeLong’s test indicated that the stacking model had a significantly higher AUC than CatBoost (p = 0.03) and Logistic Regression (p = 0.01), whereas its difference from ExtraTrees was not statistically significant (p = 0.47). Conclusions Mammography-based delta radiomics combined with machine learning showed promising performance for predicting pCR after NACT in breast cancer patients; however, external validation is needed to confirm clinical applicability.",
  "abstract_shown": true,
  "affiliations": [
    "Department of MR-1, Affiliated Hospital of North China University of Science and Technology",
    "Department of General Surgery Ward 4 (Breast), Liuzhou Work’s Hospital",
    "Department of Archives, North China University of Science and Technology",
    "Department of Breast Surgery-3, Tangshan People’s Hospital",
    "Department of Breast Surgery-4, Tangshan People’s Hospital",
    "Department of Breast Center, Tangshan People’s Hospital (Hebei Key Laboratory of Molecular Oncology, Affiliated Tangshan People’s Hospital of North China University of Science and Technology)"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xiaohang Hu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yanfang Liu"
    },
    {
      "affs": [
        2
      ],
      "name": "Yuan Gao"
    },
    {
      "affs": [
        3
      ],
      "name": "Yuhui Ge"
    },
    {
      "affs": [
        3
      ],
      "name": "Yang Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Yongping Du"
    },
    {
      "affs": [
        4
      ],
      "name": "Nan Zhang"
    },
    {
      "affs": [
        4
      ],
      "name": "Dandan Cao"
    },
    {
      "affs": [
        3
      ],
      "name": "Haiping Zhang"
    },
    {
      "affs": [
        5
      ],
      "name": "Qi Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dz615824a49hq.bib",
  "cited_by": 0,
  "doi": "10.1007/s12672-026-05645-2",
  "fulltext_url": "https://doi.org/10.1007/s12672-026-05645-2",
  "id": "dz615824a49hq",
  "issns": [
    "2730-6011"
  ],
  "landing_url": "https://doi.org/10.1007/s12672-026-05645-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC254-282"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
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  "openalex": "W7171354715",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-01",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/dz615824a49hq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:46:06Z",
      "native_id": "cd78cbfce4b94951983159831c847832",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s12672-026-05645-2"
    }
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Mammography based delta radiomics with machine learning predicts pathologic complete response after neoadjuvant chemotherapy in breast cancer",
  "title_text": "Mammography based delta radiomics with machine learning predicts pathologic complete response after neoadjuvant chemotherapy in breast cancer",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:06Z",
  "url": "https://research.draconitic.com/paper/dz615824a49hq",
  "venue": "Discover Oncology",
  "versions": []
}