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)
- 1Department of General and Transplant Surgery, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland
- 2Department of Pulmonology, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland
- 3Department of Internal Medicine, Asthma and Allergy, Norbert Barlicki University Hospital, Medical University of Łódź, 90-153 Łódź, Poland
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.
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BibTeX
@article{wardeszkiewicz2026simple,
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)}},
author = {Aleksander Wardęszkiewicz and Konrad Kosztowny and Oliwia Grząsiak-Kraj and Piotr Hogendorf and Adam Durczyński and Anna Kumor-Kisielewska and Michał Panek and Janusz Strzelczyk and Alicja Majos},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18213477},
url = {https://www.mdpi.com/2072-6694/18/21/3477},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - 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)
AU - Aleksander Wardęszkiewicz
AU - Konrad Kosztowny
AU - Oliwia Grząsiak-Kraj
AU - Piotr Hogendorf
AU - Adam Durczyński
AU - Anna Kumor-Kisielewska
AU - Michał Panek
AU - Janusz Strzelczyk
AU - Alicja Majos
PY - 2026
DA - 2026/10/28
JO - Cancers
DO - 10.3390/cancers18213477
UR - https://www.mdpi.com/2072-6694/18/21/3477
AB - 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.
SN - 2072-6694
ER -