Predicting Cancer: A Mathematical Model for Breast Cancer Progression
Overview
Breast cancer continues to be researched, as it is still incurable. The grading system for breast cancer, while based off of numbers, is subjective as it depends upon the interpretation of the physician. To make this process more objective, a previous study investigated the possibility of incorporating fractal dimensions into the process and displayed promising results. To build, there are two purposes for the current study: first, to make the calculation process quicker using computer programs to measure fractal dimensions and second, to implement the use of fractal dimensions into a mathematical model that would predict the progression of a breast cancer tumor. It is common to hear that cancer is an unsystematic and chaotic ailment that cannot be modeled, however it was hypothesized that if fractal dimensions, along with other elements used during the diagnosis process, are used together to create a mathematical model, then the model could predict the progression and possibility of relapse of the tumor. For the first purpose, a computer program was used to analyze images of tissue for their fractal dimensions, which were then compared to the fractal dimensions found from the manual process. A percent difference calculation provided a difference value between computer-generated and manually calculated fractal dimensions. Research continues with the second purpose, as this is a long-term project and will require follow-up studies. The results obtained in this study show that the computer-generated fractal dimensions match with the manually found fractal dimensions, showing promise for the use of computer programs in the analysis. However, this is based off of a very minimal sample size and more results will need to be interpreted before a firm conclusion is found.
Competition history
- ISEF 2015
Resources
Related projects
ISEF · 2014
Using the Fractal Dimension to Diagnose Cancer
ISEF · 2015
Fractal Dimension Applied to radiographic Study of Nodules in the Breast
ISEF · 2024
Accelerating Breast Cancer Diagnoses: Leveraging Machine Learning to Accurately Predict Breast Cancer Presence and Growth With Hormonal Data
ISEF · 2022
Chaos in Cancer: Analyzing Network Morphology To Predict Tumor Angiogenesis Using Chaos Theory
ISEF · 2014
Quantitative Analysis of Macro-Cellular Biomarkers in Early Stage Ductal Carcinoma in situ (DCIS) Immunohistochemical Cytopathology Images Using Machine Learning
ISEF · 2026
Early Stage Analysis of Alzheimer's Disease Using Fractal Dimension Calculations
ISEF · 2023
Improving Contemporary Mathematical Models of Metastatic Cancer to Predict Optimal Treatment: Analyzing Glycolysis, Treatment, and PACC Quiescence
ISEF · 2025
Spatial Tumor-Immune Analysis: Insights From Pathology Slides and Breast Cancer Survival
Closest projects by meaning, across every fair and year in the corpus.
Browse more like this
Source: Regeneron International Science and Engineering Fair