Exploring the Diagnostic Potential of Radiomics-Based PET Image Analysis for T-Stage Tumor Diagnosis
Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
8-2024
Committee Chair or Co-Chairs
Mostafa Zahed
Committee Members
Michele Joyner, Robert Price
Abstract
Cancer is a leading cause of death globally, and early detection is crucial for better
outcomes. This research aims to improve Region Of Interest (ROI) segmentation
and feature extraction in medical image analysis using Radiomics techniques
with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including
PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance Test. The dataset consisted of CT and PET-CT DICOM images from 131 lung cancer patients. Results showed that PCA identified 14 features, Hierarchical Clustering 17, t-SNE 58, and ISOMAP 40, with texture-based features being the most critical. This study highlights the potential of integrating Radiomics and unsupervised learning techniques to enhance cancer prediction from medical images.
Document Type
Thesis - unrestricted
Recommended Citation
Aderanti, Victor, "Exploring the Diagnostic Potential of Radiomics-Based PET Image Analysis for T-Stage Tumor Diagnosis" (2024). Electronic Theses and Dissertations. Paper 4455. https://dc.etsu.edu/etd/4455
Copyright
Copyright by the authors.
Included in
Biostatistics Commons, Data Science Commons, Statistical Models Commons, Vital and Health Statistics Commons