Medicine and Health Sciences Commons^™

Targeting Tgf-Β During Epithelial-To-Mesenchymal Progression As An Effective Therapy Against Colorectal Cancer, Joyce Fan

Undergraduate Research

Colorectal cancer is one of the most common cancers worldwide. Understanding the mechanisms of colorectal cancer progression is crucial for the development of effective therapeutics. Epithelial-to-mesenchymal transition is a hallmark feature of cancer and is defined as the loss of epithelial cell features, such as apical-basal polarity and high expression of cell adhesion molecules, and the development of mesenchymal features, such as lack of polarity and increased cell mobility. Epithelial-to-mesenchymal is essential for cell migration, proliferation, and tumor growth. Both the TGF-β and SMAD pathway are associated with colorectal cancer progression. TGF-β is crucial to the cellular mechanism of cell …

Classification Of Colorectal Cancer Using Resnet And Efficientnet Models, Abhishek Ranjan, Priyanshu Srivastva, B Prabadevi, R Sivakumar, Rahul Soangra, Shamala K. Subramaniam

Physical Therapy Faculty Articles and Research

Introduction:

Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells.

Aim:

Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural Networks (CNNs) on histological images.

Methods:

Despite challenges in …

Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando

Community & Environmental Health Faculty Publications

Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …