{
  "abstract": "BackgroundChemotherapy (CT) de-escalation strategies based on dual HER2 blockade with trastuzumab and pertuzumab (HP) have shown promising efficacy in HER2-positive (HER2+) early breast cancer (EBC), but biomarkers to guide patient selection are lacking. The Ataraxis Breast platform (ATX) uses artificial intelligence (AI) models integrating hematoxylin-eosin (HCE) whole-slide images and clinical variables to predict pathological complete response (pCR) and long-term outcomes. We evaluated ATX in the phase II PHERGain (NCT03161353) and PHERGain-2 (NCT04733118) trials.\n\nMethodsThe ATX NEO model was assessed for pCR prediction in PHERGain (n=306 evaluable patients) and PHERGain-2 (n=383 evaluable patients). The ATX RISK model was evaluated for prognosis in PHERGain (n=309 evaluable patients). Prespecified cutoffs classified patients into low-, medium-, and high-probability groups for pCR and low-or high-risk groups for recurrence. Associations with pCR were assessed using logistic regression. Prognostic performance was evaluated using Cox proportional hazards models, Kaplan-Meier estimates, and Harrells C-index.\n\nResultsOverall pCR rates were 40.2% in PHERGain and 60.3% in PHERGain-2. Continuous NEO scores were significantly associated with pCR in both PHERGain (odds ratio [OR], 1.28; 95% CI, 1.11-1.48; P<0.001) and PHERGain-2 (OR, 1.30; 95% CI, 1.13-1.52; P<0.001). Using prespecified cutoffs, pCR rates increased across low-, medium-, and high-probability groups (19.2%, 41.3%, and 48.3% in PHERGain; 29.5%, 61.6%, and 70.1% in PHERGain-2). In PHERGain, the RISK score was significantly associated with event-free survival (hazard ratio per 1-SD increase, 1.56; 95% CI, 1.17-2.08; P=0.002; C-index, 0.66). Five-year event-free survival was 93.9% in the low-risk group and 83.1% in the high-risk group.\n\nConclusionsNEO score was consistently associated with pCR across two independent HER2+ EBC trials, while RISK score provided prognostic information for long-term outcomes in PHERGain. Prespecified score categories identified clinically distinct response and risk groups. These findings support further evaluation of AI-based biomarkers to refine patient selection for CT de-escalation strategies in HER2+ EBC.",
  "abstract_shown": true,
  "affiliations": [
    "Hospital Arnau De Vilanova, FISABIO, Valencia, Spain"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 32,
  "authors": [
    {
      "name": "M. Ruiz Borrego"
    },
    {
      "name": "J. Cortes"
    },
    {
      "name": "A. Stradella"
    },
    {
      "name": "B. Bermejo"
    },
    {
      "name": "S. Escriva de Romani"
    },
    {
      "name": "C. Reboredo"
    },
    {
      "name": "C. Albacar"
    },
    {
      "name": "L. Garrigos"
    },
    {
      "name": "S. Kummel"
    },
    {
      "name": "M. Colleoni"
    },
    {
      "name": "G. Gebhart"
    },
    {
      "name": "F. Dalenc"
    },
    {
      "name": "K. Kerrou"
    },
    {
      "name": "S. Braga"
    },
    {
      "name": "P. Schmid"
    },
    {
      "name": "F. Marme"
    },
    {
      "name": "S. Di Cosimo"
    },
    {
      "name": "G. Notini"
    },
    {
      "name": "E. Martinez Garcia"
    },
    {
      "name": "A. Amaya-Garrido"
    },
    {
      "name": "D. Alcala Lopez"
    },
    {
      "name": "L. Mina"
    },
    {
      "name": "J. Rodriguez Morato"
    },
    {
      "name": "C. Tang"
    },
    {
      "name": "D. Biswas"
    },
    {
      "name": "B. Machura"
    },
    {
      "name": "J. Witowski"
    },
    {
      "name": "K. J. Geras"
    },
    {
      "name": "J. Park"
    },
    {
      "name": "M. Mancino"
    },
    {
      "name": "J. M. Perez Garcia"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Llombart Cussac"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6hfax994dq28f.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.05.26364736",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26364736v1.full",
  "id": "6hfax994dq28f",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26364736v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "oncology"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7221058815",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26364736v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/6hfax994dq28f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:22Z",
      "native_id": "10.64898/2026.10.05.26364736",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.05.26364736v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Multimodal Artificial Intelligence Predicts Pathological Response and Prognosis in HER2-Positive Early Breast Cancer Treated with Chemotherapy De-escalation Strategies: Analyses from PHERGain and PHERGain-2 Trials",
  "title_text": "Multimodal Artificial Intelligence Predicts Pathological Response and Prognosis in HER2-Positive Early Breast Cancer Treated with Chemotherapy De-escalation Strategies: Analyses from PHERGain and PHERGain-2 Trials",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:22Z",
  "url": "https://research.draconitic.com/paper/6hfax994dq28f",
  "venue": "medRxiv",
  "versions": []
}