{
  "abstract": "We present an automated image-based framework for quantifying biomarker expression in immunohistochemical (IHC) images and introduce the Marker Expression Level (MEL) score as an objective alternative to visual H-scoring. MEL addresses key limitations of existing automated methods by integrating tissue-region segmentation with tumour-area-weighted marker quantification, normalization to mitigate inter-sample and inter-batch staining variability, and robust quantification of nuclear, cytoplasmic, and membrane-associated biomarkers. The complete pipeline was developed as an open-source Fiji/ImageJ tool and is publicly available.\n\nWe evaluated MEL using IHC images from 128 lung adenocarcinoma patients stained for BRCA1, GLUT1, and QKI, three markers forming a prognostic signature previously described by our group. Tissue regions were segmented using a machine learning-based Fiji plugin, and MEL scores were calculated within tumour regions. A normalization strategy accounted for inter-TMA staining variability. Agreement with visual H-scores was assessed using Spearman correlation. Prognostic performance was evaluated using Cox regression to derive a Combined Prognostic Index (CPI) incorporating MEL scores and tumour stage.\n\nSegmentation achieved high accuracy across tissue regions (Dice coefficients 0.69-0.99), with lower performance in highly immune-infiltrated stromal areas. MEL scores significantly correlated with visual H-scores for all markers ({rho} = 0.61-0.69, p < 0.001). Normalization improved agreement for GLUT1 ({rho} = 0.39 to 0.62). The derived CPI stratified patients into distinct risk groups with significant differences in disease-free and overall survival (p < 0.05), comparable to visual scoring.\n\nMEL provides a reproducible and scalable alternative to expert visual H-scoring and standard semi-automated approaches such as QuPath. Its integration with prognostic modelling reproduced a validated survival signature with performance comparable to expert scoring, supporting clinical and translational applicability. MEL is not a marginal refinement of existing digital scoring methods, but rather a clinically oriented and biologically adaptable framework designed to improve the robustness and reproducibility of automated IHC quantification.",
  "abstract_shown": true,
  "affiliations": [
    "Public University of Navarra"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "F. Carraro"
    },
    {
      "name": "M. Sanguesa"
    },
    {
      "name": "A. Irisarri"
    },
    {
      "name": "D. Orive"
    },
    {
      "name": "C. Sainz"
    },
    {
      "name": "I. Arganda-Carreras"
    },
    {
      "name": "A. Calvo"
    },
    {
      "name": "L. M. Montuenga"
    },
    {
      "name": "C. Ortiz de Solorzano"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Ariz"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9a2bkkxm3eg6m.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.26366204",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366204v1.full",
  "id": "9a2bkkxm3eg6m",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366204v1",
  "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",
      "health informatics"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7223693216",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366204v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9a2bkkxm3eg6m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:51Z",
      "native_id": "10.64898/2026.10.22.26366204",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366204v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Development and Validation of an Automated Image-Based Method for Prognostic Stratification in Lung Adenocarcinoma using Immunohistochemical Markers",
  "title_text": "Development and Validation of an Automated Image-Based Method for Prognostic Stratification in Lung Adenocarcinoma using Immunohistochemical Markers",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:51Z",
  "url": "https://research.draconitic.com/paper/9a2bkkxm3eg6m",
  "venue": "medRxiv",
  "versions": []
}