MATHEMATICAL MODEL FOR TISSUE PATHOLOGIES

To overcome the time intensive and protocol dependent nature of traditional histopathological analysis for tumor identification, a mathematical model can be developed based on the distinct density profiles of different tissue types. The foundation of this model lies in assigning quantitative density values: healthy tissue would be characterized by a relatively consistent and organ-specific density range, serving as a baseline. Lipoma, being composed of adipose tissue which is less dense than water, would be mathematically defined by a significantly lower density value. In contrast, a cancerous or malignant tumor, with its disorganized architecture, higher cellular density, and regions of necrosis and irregular angiogenesis, would be represented by a density that is both different and typically more heterogeneous than healthy tissue or lipomas. By analyzing tissue samples through imaging or other modalities that infer density, this model could rapidly classify tissue as healthy, lipomatous, or potentially malignant, providing a crucial and expedited initial screening tool to guide further diagnostic efforts.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-12
DOI
https://doi.org/10.5281/zenodo.17587241
Primary Topic
Mathematical Biology Tumor Growth
Type
article
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article

MATHEMATICAL MODEL FOR TISSUE PATHOLOGIES

Zenodo (CERN European Organization for Nuclear Research)
Mathematical Biology Tumor Growth
article

MATHEMATICAL MODEL FOR TISSUE PATHOLOGIES

article en

Abstract

To overcome the time intensive and protocol dependent nature of traditional histopathological analysis for tumor identification, a mathematical model can be developed based on the distinct density profiles of different tissue types. The foundation of this model lies in assigning quantitative density values: healthy tissue would be characterized by a relatively consistent and organ-specific density range, serving as a baseline. Lipoma, being composed of adipose tissue which is less dense than water, would be mathematically defined by a significantly lower density value. In contrast, a cancerous or malignant tumor, with its disorganized architecture, higher cellular density, and regions of necrosis and irregular angiogenesis, would be represented by a density that is both different and typically more heterogeneous than healthy tissue or lipomas. By analyzing tissue samples through imaging or other modalities that infer density, this model could rapidly classify tissue as healthy, lipomatous, or potentially malignant, providing a crucial and expedited initial screening tool to guide further diagnostic efforts.

Zenodo (CERN European Organization for Nuclear Research)
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Mathematical Biology Tumor Growth
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