Application of fractal theory in cancer detection: A review

Fractal theory has emerged as a quantitative framework for characterizing the complex and heterogeneous architecture of cancerous tissues; however, its role in cancer detection remains scattered across cancer types, methods, and clinical contexts. This review provides a unified and critical synthesis of fractal theory applications in cancer detection, integrating evidence from breast, lung, prostate, and skin cancers. Rather than treating fractal metrics in isolation, the review positions fractal analysis as a scale-invariant paradigm for quantifying tumor-associated structural disorder across imaging and histopathology. Key methodologies, including fractal dimension, multifractal analysis, and fractal texture descriptors, are evaluated alongside the maturity of existing evidence and their relationship to established diagnostic tools. Current limitations in standardization and clinical validation are identified, and future directions for integrating fractal features with artificial intelligence frameworks are outlined to support translational and decision-oriented cancer diagnostics.