Unsupervised Dimension Reduction Techniques for Lung Cancer Diagnosis Based on Radiomics
Location
Culp Center Ballroom
Start Date
4-25-2023 9:00 AM
End Date
4-25-2023 11:00 AM
Poster Number
33
Faculty Sponsor’s Department
Mathematics & Statistics
Name of Project's Faculty Sponsor
Mostafa Zahed
Competition Type
Competitive
Type
Poster Presentation
Project's Category
Cancer or Carcinogenesis
Abstract or Artist's Statement
One of the most pressing global health concerns is the impact of cancer, which remains a leading cause of death worldwide. The timeliness of detection and diagnosis is critical to maximizing the chances of successful treatment. Radiomics is an emerging medical imaging analysis proposed, which refers to the high-throughput extraction of a large number of image features. Radiomics generally refers to the use of CT, PET, MRI or Ultrasound imaging as input data, extracting expressive features from massive image-based data, and then using machine learning or statistical models for quantitative analysis and prediction of disease. Feature reduction is very critical in Radiomics as a large number of quantitative features can have redundant characteristics not necessarily important in the analysis process. Due to the immense features obtained from radiological images, the main objective of our research is the application of machine learning techniques to reduce the number of dimensions, thereby rendering the data more manageable. Radiomics involves several steps including: Imaging, segmentation, feature extraction, and analysis. Extracted features can be categorized in the description of tumor gray histograms, shape, texture features, and the tumor location and surrounding tissue. For this research, a large-scale CT dataset for Lung cancer diagnosis (Lung- PET-CT-Dx) which was collected by scholars from Medical University in Harbin in China is used to illustrate the dimension reduction techniques, which is a main part of radiomics process, via R, SAS and Python. The proposed reduction and analysis techniques in our research will entail; Principal Component Analysis, Clustering analysis (Hierarchical Clustering and K-means), and Manifold-based algorithms (Isometric Feature Mapping (ISOMAP).
Unsupervised Dimension Reduction Techniques for Lung Cancer Diagnosis Based on Radiomics
Culp Center Ballroom
One of the most pressing global health concerns is the impact of cancer, which remains a leading cause of death worldwide. The timeliness of detection and diagnosis is critical to maximizing the chances of successful treatment. Radiomics is an emerging medical imaging analysis proposed, which refers to the high-throughput extraction of a large number of image features. Radiomics generally refers to the use of CT, PET, MRI or Ultrasound imaging as input data, extracting expressive features from massive image-based data, and then using machine learning or statistical models for quantitative analysis and prediction of disease. Feature reduction is very critical in Radiomics as a large number of quantitative features can have redundant characteristics not necessarily important in the analysis process. Due to the immense features obtained from radiological images, the main objective of our research is the application of machine learning techniques to reduce the number of dimensions, thereby rendering the data more manageable. Radiomics involves several steps including: Imaging, segmentation, feature extraction, and analysis. Extracted features can be categorized in the description of tumor gray histograms, shape, texture features, and the tumor location and surrounding tissue. For this research, a large-scale CT dataset for Lung cancer diagnosis (Lung- PET-CT-Dx) which was collected by scholars from Medical University in Harbin in China is used to illustrate the dimension reduction techniques, which is a main part of radiomics process, via R, SAS and Python. The proposed reduction and analysis techniques in our research will entail; Principal Component Analysis, Clustering analysis (Hierarchical Clustering and K-means), and Manifold-based algorithms (Isometric Feature Mapping (ISOMAP).