Decoding Breast Cancer in X-ray Mammograms: A Multi-Parameter Approach Using Fractals, Multifractals, and Structural Disorder Analysis.

Santanu Maity, Mousa Alrubayan, Prabhakar Pradhan

Abstract readPreprint

In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Santanu MaityDepartment of Physics and Astronomy, Mississippi State University, Mississippi State, MS, USA, 39762.

Mousa AlrubayanDepartment of Physics and Astronomy, Mississippi State University, Mississippi State, MS, USA, 39762.

Prabhakar PradhanDepartment of Physics and Astronomy, Mississippi State University, Mississippi State, MS, USA, 39762.

Funding

Photonics probing of DNA mass density spatial structure for cancer diagnosticsR21CA260147 · NCI · MISSISSIPPI STATE UNIVERSITY · PI PRADHAN, PRABHAKAR · 2021 to 2021

$397k

NCI NIH HHS R21 CA260147

6 · The paper itself

Abstract

We explored the fractal and multifractal characteristics of breast mammogram micrographs to identify quantitative biomarkers associated with breast cancer progression. In addition to conventional fractal and multifractal analyses, we employed a recently developed fractal-functional distribution method, which transforms fractal measures into Gaussian distributions for more robust statistical interpretation. Given the sparsity of mammogram intensity data, we also analyzed how variations in intensity thresholds, used for binary transformations of the fractal dimension, follow unique trajectories that may serve as novel indicators of disease progression. Our findings demonstrate that fractal, multifractal, and fractal-functional parameters effectively differentiate between benign and cancerous tissue. Furthermore, the threshold-dependent behavior of intensity-based fractal measures presents distinct patterns in cancer cases. To complement these analyses, we applied the Inverse Participation Ratio (IPR) light localization technique to quantify structural disorder at the microscopic level. This multi-parametric approach, integrating spatial complexity and structural disorder metrics, offers a promising framework for enhancing the sensitivity and specificity of breast cancer detection.

Identifiers

PMID40492247

PMCPMC12148087

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.