{
  "abstract": "Objective: To evaluate the incremental predictive value of pretreatment tri-modal ultrasound deep learning integrating two-dimensional ultrasound (2DUS), color Doppler flow imaging (CDFI), and contrast-enhanced ultrasound (CEUS) for predicting breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC), and to assess performance stability across independent centers. Methods: This retrospective multicenter study included 1089 patients with primary invasive breast cancer. Center 1 provided the training cohort (n = 572) and internal validation cohort (n = 246), whereas centers 2 (n = 160) and 3 (n = 111) served as independent external validation cohorts. Using pretreatment 2DUS, CDFI, and CEUS within a standardized intratumoral and 10-mm peritumoral framework, single-modality, all three dual-modality combinations, and tri-modal models were developed. The best-performing dual-modality model was identified and locked during training as the primary imaging comparator. A Tri-modal + Clinical model further integrated pretreatment clinical and biomarker variables. Performance was assessed using AUC, Brier score, calibration, decision curve analysis, and ablation experiments. Results: Overall, 383 patients (35.2%) achieved bpCR. CEUS was the strongest single modality, and 2DUS + CEUS was the best-performing dual-modality model. Compared with 2DUS + CEUS, the tri-modal model increased the AUC from 0.895 to 0.919 in the combined external cohort (ΔAUC, +0.023; 95% CI, +0.010 to +0.038; p = 0.001), with corresponding improvements in Brier score (0.124 to 0.105) and F1 score (0.747 to 0.786). The AUC point estimate increased in both external centers, although the 95% CI for the incremental AUC included zero in external validation cohort 2. Despite modest standalone discrimination, CDFI provided measurable incremental information when added to 2DUS + CEUS. Further integration of pretreatment clinical and biomarker variables increased the combined external AUC to 0.935. Reduced-input retraining showed performance decreases after omission of any modality, with the largest reduction after CEUS omission. Conclusions: Pretreatment tri-modal ultrasound deep learning provided incremental predictive value for bpCR beyond the best dual-modality model, with a consistent direction of benefit across two independent external centers. Clinical and biomarker integration provided additional predictive information, supporting further evaluation of this multimodal framework for individualized pretreatment bpCR prediction.",
  "abstract_shown": true,
  "affiliations": [
    "Ultrasound Medical Center, The Second Hospital of Lanzhou University, Lanzhou 730030, China",
    "Department of Ultrasound Diagnosis, Gansu Provincial Maternity and Child-Care Hospital (Gansu Provincial Central Hospital), Lanzhou 730050, China",
    "Department of Ultrasound, Gansu Provincial Cancer Hospital, Lanzhou 730050, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Chuanmin Wei",
      "orcid": "0009-0003-4665-769X"
    },
    {
      "affs": [
        1
      ],
      "name": "Ting Pan"
    },
    {
      "affs": [
        1
      ],
      "name": "Tiangang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Chunyao Zheng"
    },
    {
      "affs": [
        2
      ],
      "name": "Ying Duan"
    },
    {
      "affs": [
        0
      ],
      "name": "Fang Nie",
      "orcid": "0000-0001-9725-1743"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3dmnr5051bppy.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18203326",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702602/?tool=EBI",
  "id": "3dmnr5051bppy",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://www.mdpi.com/2072-6694/18/20/3326",
  "language": "eng",
  "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": [
    [
      "openalex",
      "2741|MRI in cancer diagnosis"
    ]
  ],
  "openalex": "W7222259377",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13702602/pdf/?tool=EBI",
  "pmcid": "PMC13702602",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3dmnr5051bppy.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "10.3390/cancers18203326",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6694/18/20/3326"
    },
    {
      "first_seen": "2026-10-30T12:21:47Z",
      "native_id": "PMC:PMC13702602",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13702602"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|MRI in cancer diagnosis",
      "subfield": "imaging"
    }
  ],
  "title": "Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study",
  "title_text": "Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:21:47Z",
  "url": "https://research.draconitic.com/paper/3dmnr5051bppy",
  "venue": "Cancers",
  "versions": []
}