Open Access. Powered by Scholars. Published by Universities.®
- Publication Type
Articles 1 - 2 of 2
Full-Text Articles in Medicine and Health Sciences
Assessment Of Mucin 13 (Muc13) As An Imaging Target For Guiding Colorectal Cancer Treatment: A Pathway Towards Theranostic Development, Ryan P. Coll, Aiko Yamaguchi, Jianbo Wang, Xiaoxia Wen, Denise Hernandez, Subhash C. Chauhan, H. Charles Manning
Assessment Of Mucin 13 (Muc13) As An Imaging Target For Guiding Colorectal Cancer Treatment: A Pathway Towards Theranostic Development, Ryan P. Coll, Aiko Yamaguchi, Jianbo Wang, Xiaoxia Wen, Denise Hernandez, Subhash C. Chauhan, H. Charles Manning
Research Symposium
Background: A theranostic strategy combining diagnostic imaging and targeted therapy in a single regimen is proposed for improved management and treatment of colorectal cancer (CRC). Increased specificity in detection by the noninvasive imaging technique positron emission tomography (PET) can be achieved by radiolabeling antibodies (Abs) designed to target tumor-associated antigens with increased expression post-translational modifications present in cancer cells. In this study, an Ab designed to target the transmembrane glycoprotein mucin 13 (MUC13) was radiolabeled with the positron-emitting radionuclide zirconium-89 (89Zr) for PET imaging of a xenograft mouse model of CRC. Specified uptake of this radioimmunoconjugate was observed …
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
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, …