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Lung cancer-a fractal viewpoint
Frances E Lennon1, Gianguido C Cianci2, Nicole A Cipriani3
1Section of Hematology/Oncology, University of Chicago, 5841 South Maryland Avenue, MC 2115 Chicago, IL 60637, USA.
Nature Reviews. Clinical Oncology
|July 15, 2015
Summary
Fractal analysis offers a powerful method to quantify lung tumor complexity and irregularity. This review introduces fractal geometry for analyzing lung cancer, aiding diagnosis and treatment.
Area of Science:
- Mathematics
- Biomedical Imaging
- Oncology
Background:
- Fractals exhibit self-similarity and fractal dimensions, useful for complex shapes.
- Traditional Euclidean geometry struggles with irregular patterns in biological systems like lung tumors.
- Fractal analysis shows promise in biomedical imaging for various physiological measurements.
Purpose of the Study:
- Introduce fractal mathematics and fractal dimension (FD) analysis.
- Explain the suitability of the lung for fractal analysis.
- Review applications of fractal analysis in lung cancer research and clinical imaging.
Main Methods:
- Quantifying geometrical complexity and irregularity using fractal geometry.
- Analyzing fractal dimension (FD) and lacunarity (texture).
- Reviewing studies on nuclear/chromatin FD in tumor cells and imaging-based FD in CT/PET scans.
Main Results:
- Fractal analysis efficiently estimates complexity in lung tumor growth patterns.
- Changes in FD correlate with tumor growth and treatment response in clinical imaging.
- Fractal analysis quantifies nuclear and chromatin alterations in tumor cells.
Conclusions:
- Fractal analysis provides valuable insights into lung cancer complexity.
- FD and lacunarity can aid in lung cancer diagnosis and therapeutic management.
- Increased awareness of fractal mathematics is needed among researchers and clinicians.
