Fractal Geometry and Fractional Calculus for Integrative Morphological Mapping of Breast Cancer Complexity

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Das, Abhijeet, Bhat, Ramray, Jolly, Mohit Kumar
Format: Preprint
Veröffentlicht: 2025
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866911267267018752
author Das, Abhijeet
Bhat, Ramray
Jolly, Mohit Kumar
author_facet Das, Abhijeet
Bhat, Ramray
Jolly, Mohit Kumar
contents Breast cancer exhibits intricate morphological and dynamical heterogeneity across cellular, tissue, and tumor scales, posing challenges to conventional modeling approaches that fail to capture its nonlinear, self-similar, or self-affine, and memory-dependent behavior. Despite increasing applications of fractal geometry and fractional calculus in cancer modeling, their methodological integration and biological interpretation remain insufficiently consolidated. This review aims to synthesize these frameworks within an integrative morphological perspective to elucidate their collective potential for quantitative characterization of breast cancer complexity. Fractal geometry-based analyses quantify spatial and temporal irregularities along with spatiotemporal morphodynamics, while fractional calculus introduces non-local and memory-dependent formulations describing tumor growth. Together, these frameworks establish a mathematical link between fractal structure and fractional dynamics. Nevertheless, their application remains hindered by inconsistent methodologies and a lack of reproducible standards. This review consolidates existing evidence, delineates methodological interrelations between fractal geometry and fractional calculus, and outlines reproducibility requirements, including standardized preprocessing, parameter reporting, and benchmark datasets. Collectively, the findings emphasize that reproducible and biologically interpretable integration of these two approaches is fundamental to achieving clinically relevant modeling of breast cancer morphology and dynamics.
format Preprint
id arxiv_https___arxiv_org_abs_2505_07338
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Fractal Geometry and Fractional Calculus for Integrative Morphological Mapping of Breast Cancer Complexity
Das, Abhijeet
Bhat, Ramray
Jolly, Mohit Kumar
Quantitative Methods
Breast cancer exhibits intricate morphological and dynamical heterogeneity across cellular, tissue, and tumor scales, posing challenges to conventional modeling approaches that fail to capture its nonlinear, self-similar, or self-affine, and memory-dependent behavior. Despite increasing applications of fractal geometry and fractional calculus in cancer modeling, their methodological integration and biological interpretation remain insufficiently consolidated. This review aims to synthesize these frameworks within an integrative morphological perspective to elucidate their collective potential for quantitative characterization of breast cancer complexity. Fractal geometry-based analyses quantify spatial and temporal irregularities along with spatiotemporal morphodynamics, while fractional calculus introduces non-local and memory-dependent formulations describing tumor growth. Together, these frameworks establish a mathematical link between fractal structure and fractional dynamics. Nevertheless, their application remains hindered by inconsistent methodologies and a lack of reproducible standards. This review consolidates existing evidence, delineates methodological interrelations between fractal geometry and fractional calculus, and outlines reproducibility requirements, including standardized preprocessing, parameter reporting, and benchmark datasets. Collectively, the findings emphasize that reproducible and biologically interpretable integration of these two approaches is fundamental to achieving clinically relevant modeling of breast cancer morphology and dynamics.
title Fractal Geometry and Fractional Calculus for Integrative Morphological Mapping of Breast Cancer Complexity
topic Quantitative Methods
url https://arxiv.org/abs/2505.07338