Blood laboratory parameters can predict relapse-free survival of patients with advanced squamous cell lung cancer and adenocarcinoma

The aim of the work was to study the relationship between the concentration of cells and proteins in the blood of patients with Stage III squamous cell lung cancer (SCLC) and adenocarcinoma (AC) before surgical treatment and the duration of the relapse-free period after tumor resection to develop prognostic models for relapse-free survival in these diseases. Using logistic regression equations, the models incorporated variables included cytokeratin 19 fragment antigen 21-1 (CYFRA 21-1) concentration, the proportion of lymphocytes expressing the C-X-C motif chemokine receptor 1 (CXCR1), and monocytes expressing the C-X-C motif chemokine receptor 2 for SCLC. For AC, the models included the CYFRA 21-1 concentration, lymphocytes expressing the CXCR1 receptor, and the eosinophil-to-monocyte ratio. These models can predict the probability of tumor recurrence based on measurements of blood parameters in the pre-operative period, with a prediction efficiency of 87.7% for SCLC and 89.0% for AC.

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Publication Details

Journal
Global Translational Medicine
Published
2024-12-13
DOI
https://doi.org/10.36922/gtm.4865
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Blood laboratory parameters can predict relapse-free survival of patients with advanced squamous cell lung cancer and adenocarcinoma

Anatoli Tahanovich, O. V. Gotko, Armin Kolb, Mikalai M. Kauhanka et al.
Global Translational Medicine
Radiomics and Machine Learning in Medical Imaging
article

Blood laboratory parameters can predict relapse-free survival of patients with advanced squamous cell lung cancer and adenocarcinoma

Anatoli Tahanovich, O. V. Gotko, Armin Kolb, Mikalai M. Kauhanka, Violetta I. Prokharova
article en

Abstract

The aim of the work was to study the relationship between the concentration of cells and proteins in the blood of patients with Stage III squamous cell lung cancer (SCLC) and adenocarcinoma (AC) before surgical treatment and the duration of the relapse-free period after tumor resection to develop prognostic models for relapse-free survival in these diseases. Using logistic regression equations, the models incorporated variables included cytokeratin 19 fragment antigen 21-1 (CYFRA 21-1) concentration, the proportion of lymphocytes expressing the C-X-C motif chemokine receptor 1 (CXCR1), and monocytes expressing the C-X-C motif chemokine receptor 2 for SCLC. For AC, the models included the CYFRA 21-1 concentration, lymphocytes expressing the CXCR1 receptor, and the eosinophil-to-monocyte ratio. These models can predict the probability of tumor recurrence based on measurements of blood parameters in the pre-operative period, with a prediction efficiency of 87.7% for SCLC and 89.0% for AC.

Global Translational MedicineVol. 3(4)
Openalex Percentile: Top 60%
Radiomics and Machine Learning in Medical Imaging
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