ReviewNature reviews. Clinical oncology2015
Lung cancer-a fractal viewpoint.
Review in Nature reviews. Clinical oncology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
- Geometric morphological parameters from cardiac magnetic resonance imaging in assessment of chronic pulmonary thromboembolism, a prospective pilot study.European radiology experimental · 2026Article
- An explainable imaging-clinical biomarker for non-small cell lung cancer prognostication based on normalised hotspot to centroid distance and [European journal of nuclear medicine and molecular imaging · 2026Article
- Complex Fluids in a Multifractal Space: Scale Covariance and the Emergence of the Fractal Force.Entropy (Basel, Switzerland) · 2026Article
- 3D fractal dimension analysis of CT imaging for microvascular invasion prediction in hepatocellular carcinoma.European radiology · 2026Article
- Diagnostic efficacy of dual-energy computed tomography-based fractal analysis for assessing extramural venous invasion/tumor deposits and peripheral nerve invasion in rectal cancer.Quantitative imaging in medicine and surgery · 2026Article
- IDH mutation prediction in non-enhancing gliomas with relaxed T2-FLAIR mismatch and fractal dimension: a two-center study.BMC medical imaging · 2026Article
- Fractal-driven self-supervised learning enhances early-stage lung cancer GTV segmentation: a novel transfer learning framework.Japanese journal of radiology · 2026Article
- Fractal Analysis of Intramuscular Adipose Tissue on CT Serves as a Novel Imaging Biomarker for Metabolic Syndrome.International journal of medical sciences · 2026Article
- The biomechanical signature of tumor invasion.Genes & diseases · 2026Review
- Nomogram based on computed tomography fractal dimension for predicting spread through air spaces in lung adenocarcinoma.Frontiers in medicine · 2026Article
- Fractal analysis of longitudinal MRI for predicting response to neoadjuvant chemotherapy in breast cancer.NPJ precision oncology · 2025Article
- Refining cancer prediction with DNA sequencing and combined machine learning approaches.Scientific reports · 2025Article
- A narrative review of imaging intratumor heterogeneity in non-small cell lung cancer: current advances.Translational lung cancer research · 2025Review
- IsJournal of developmental biology · 2025Review
- Recent Insights into the Roles of PEST-Containing Nuclear Protein.Molecular biotechnology · 2025Review
- Higher density of compact B cell clusters in invasive front may contribute to better prognosis in pancreatic ductal adenocarcinoma.Discover oncology · 2025Article
- Recurrence and non-improvement of European Heart Rhythm Association symptom scores after atrial fibrillation ablation: the role of left atrial fractal dimension.Quantitative imaging in medicine and surgery · 2025Article
- Fractal analysis of healthy and diseased vasculature in pediatric Moyamoya disease.Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences · 2025Article
- Study of prediction model for high-grade meningioma using fractal geometry combined with radiological features.Journal of neuro-oncology · 2025Article
- Regulation of PEST-containing nuclear proteins in cancer cells: implications for cancer biology and therapy.Frontiers in oncology · 2025Review
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Fractals are mathematical constructs that show self-similarity over a range of scales and non-integer (fractal) dimensions. Owing to these properties, fractal geometry can be used to efficiently estimate the geometrical complexity, and the irregularity of shapes and patterns observed in lung tumour growth (over space or time), whereas the use of traditional Euclidean geometry in such calculations is more challenging. The application of fractal analysis in biomedical imaging and time series has shown considerable promise for measuring processes as varied as heart and respiratory rates, neuronal cell characterization, and vascular development. Despite the advantages of fractal mathematics and numerous studies demonstrating its applicability to lung cancer research, many researchers and clinicians remain unaware of its potential. Therefore, this Review aims to introduce the fundamental basis of fractals and to illustrate how analysis of fractal dimension (FD) and associated measurements, such as lacunarity (texture) can be performed. We describe the fractal nature of the lung and explain why this organ is particularly suited to fractal analysis. Studies that have used fractal analyses to quantify changes in nuclear and chromatin FD in primary and metastatic tumour cells, and clinical imaging studies that correlated changes in the FD of tumours on CT and/or PET images with tumour growth and treatment responses are reviewed. Moreover, the potential use of these techniques in the diagnosis and therapeutic management of lung cancer are discussed.
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