Speaker
Description
Cancer, a pervasive global health concern, particularly affects the gastrointestinal (GI) tract, contributing to a significant portion of cancer cases worldwide. Successful treatment strategies necessitate an understanding of cancer heterogeneity, which spans both inter- and intra-tumor variability. Despite extensive research on genetic and cellular heterogeneity, morphological diversity in tumors remains underexplored. Tumor organoids, three-dimensional models mirroring in vivo tumors, offer a promising avenue for investigating tumor dynamics and serve as ethical alternatives to animal testing. This thesis investigates cancer heterogeneity through a per-vertex analysis of four established biomarkers--DAPI, phalloidin, Ki67, and PHH3--coupled with curvature estimation. These biomarkers, indicative of various cellular processes, offer insight into tumor behavior and dynamics. Additionally, shape analysis through curvature estimation provides structural information crucial for understanding tumor morphology. The derived features from this analysis are then utilized to predict tumor progression stages using machine learning techniques. By integrating morphological and biomarker data, this study offers a holistic approach to characterizing tumor organoids and elucidating their potential implications for cancer research and treatment strategies.
| Authors | Maleeha Hassan*, Johannes Soltwedel, Natalie Dye, Sanika Jaharigdar |
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| Keywords | Organoids, Machine Learning, Mesh analysis, Cancer Heterogeneity |
