THE ROLE OF MSCT-RADIOMETRIC TEXTURE CRITERIA IN THE DIFFERENTIAL DIAGNOSIS OF PANCREATIC PATHOLOGIES

This study evaluates the potential of radiometric texture analysis (radiomics) of multislice computed tomography (MSCT) images in differentiating between malignant (adenocarcinoma, neuroendocrine tumors) and benign (chronic pancreatitis, cysts) pancreatic pathologies. Unlike conventional visual assessment methods, radiometric criteria (first- and higher-order texture matrices: entropy, homogeneity, asymmetry) enable the quantification of imperceptible microstructural tissue heterogeneity using mathematical models. The results demonstrated that radiometric texture analysis provides high accuracy in the differential diagnosis of visually similar hypodense lesions (sensitivity – 88.5%, specificity – 84.2%). The clinical implementation of this method reduces the number of invasive procedures, enhances early-stage disease detection, and improves the efficacy of personalized treatment strategy selection for patients.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-15
DOI
https://doi.org/10.5281/zenodo.23398580
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

THE ROLE OF MSCT-RADIOMETRIC TEXTURE CRITERIA IN THE DIFFERENTIAL DIAGNOSIS OF PANCREATIC PATHOLOGIES

Munisa Normurod qizi Ikromova
Zenodo (CERN European Organization for Nuclear Research)
Radiomics and Machine Learning in Medical Imaging
article

THE ROLE OF MSCT-RADIOMETRIC TEXTURE CRITERIA IN THE DIFFERENTIAL DIAGNOSIS OF PANCREATIC PATHOLOGIES

Munisa Normurod qizi Ikromova
article en

Abstract

This study evaluates the potential of radiometric texture analysis (radiomics) of multislice computed tomography (MSCT) images in differentiating between malignant (adenocarcinoma, neuroendocrine tumors) and benign (chronic pancreatitis, cysts) pancreatic pathologies. Unlike conventional visual assessment methods, radiometric criteria (first- and higher-order texture matrices: entropy, homogeneity, asymmetry) enable the quantification of imperceptible microstructural tissue heterogeneity using mathematical models. The results demonstrated that radiometric texture analysis provides high accuracy in the differential diagnosis of visually similar hypodense lesions (sensitivity – 88.5%, specificity – 84.2%). The clinical implementation of this method reduces the number of invasive procedures, enhances early-stage disease detection, and improves the efficacy of personalized treatment strategy selection for patients.

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
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