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The Role of Msct-radiometric Texture Criteria in the Differential Diagnosis of Pancreatic Pathologies

Year: 2026

Type: article

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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.23398580

Author: Ikromova, Munisa Normurod qizi

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7222196444

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