{
  "abstract": "Introduction: Eosinophils have become important therapeutic targets in several allergic diseases. This study aimed to apply a similar approach—using peripheral blood findings to identify features of type 2 (T2) inflammation—to oncology. We used Simple Algorithm for Blood Morphology Analysis (SAMBA) to describe blood count profiles in a cohort of patients with PDAC independently of relative ratios and propose a potential classification. Materials and Methods: We included 153 treatment-naïve patients with PDAC confirmed by postoperative histopathology. Of these, n = 68 (44.4%) underwent curative surgery, whereas n = 85 (55.6%) received palliative treatment or underwent surgical biopsy only. SAMBA was applied using laboratory reference ranges and overall survival (OS; months) as the analytical context. Results: The subgroups defined according to laboratory reference ranges did not differ significantly in disease stage, grade, or resection status. Based on correlation analyses, Kaplan–Meier curves, and a classification and regression tree (CART) analysis of OS, we propose four blood count phenotypes: exhausted, lymphopenic, T2-low, and T2-high (median OS: 1.5 months, n = 22; 4.5 months, n = 12; 8.0 months, n = 25; and 10.0 months, n = 94, respectively). Conclusions: We propose the following preliminary interpretations of these phenotypes: exhausted, potentially associated with concomitant infection or marked inflammation; lymphopenic, characterized by a low lymphocyte count and potentially reduced antitumor immune activity; eosinopenic (T2-low), characterized by a relatively balanced inflammatory profile but limited T2-associated features; and eosinophilic (T2-high), characterized by no major abnormalities in the other blood count parameters and a more favorable prognosis. SAMBA is a transparent, readily interpretable algorithm that may help standardize and accelerate preliminary research in immuno-oncology.",
  "abstract_shown": true,
  "affiliations": [
    "Department of General and Transplant Surgery, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland",
    "Department of Pulmonology, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland",
    "Department of Internal Medicine, Asthma and Allergy, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Aleksander Wardęszkiewicz"
    },
    {
      "affs": [
        0
      ],
      "name": "Konrad Kosztowny"
    },
    {
      "affs": [
        0
      ],
      "name": "Oliwia Grząsiak-Kraj",
      "orcid": "0000-0003-3383-9008"
    },
    {
      "affs": [
        0
      ],
      "name": "Piotr Hogendorf"
    },
    {
      "affs": [
        0
      ],
      "name": "Adam Durczyński"
    },
    {
      "affs": [
        1
      ],
      "name": "Anna Kumor-Kisielewska"
    },
    {
      "affs": [
        2
      ],
      "name": "Michał Panek"
    },
    {
      "affs": [
        0
      ],
      "name": "Janusz Strzelczyk"
    },
    {
      "affs": [
        0,
        2
      ],
      "name": "Alicja Majos",
      "orcid": "0000-0002-6554-1831"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3tqbw1fc8br4v.bib",
  "cited_by": 0,
  "doi": "10.3390/cancers18213477",
  "fulltext_url": null,
  "id": "3tqbw1fc8br4v",
  "issns": [
    "2072-6694"
  ],
  "landing_url": "https://www.mdpi.com/2072-6694/18/21/3477",
  "language": null,
  "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",
      "2730|Inflammatory Biomarkers in Disease Prognosis"
    ]
  ],
  "openalex": "W7224089404",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-28",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/3tqbw1fc8br4v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:58Z",
      "native_id": "10.3390/cancers18213477",
      "source": "crossref",
      "url": "https://www.mdpi.com/2072-6694/18/21/3477"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Inflammatory Biomarkers in Disease Prognosis",
      "subfield": "oncology"
    }
  ],
  "title": "Simple Algorithm for Blood Morphology Analysis (SAMBA): An Artificial Intelligence-Assisted Approach to Identify Peripheral Blood Phenotypes in a Cohort of Patients with Pancreatic Ductal Adenocarcinoma (PDAC)",
  "title_text": "Simple Algorithm for Blood Morphology Analysis (SAMBA): An Artificial Intelligence-Assisted Approach to Identify Peripheral Blood Phenotypes in a Cohort of Patients with Pancreatic Ductal Adenocarcinoma (PDAC)",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:58Z",
  "url": "https://research.draconitic.com/paper/3tqbw1fc8br4v",
  "venue": "Cancers",
  "versions": []
}