Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
The spatial organization of tumor cells, stroma, and tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment plays a critical role in cancer progression and is strongly associated with clinical outcomes. However, quantifying the significance and statistical impact of these spatial patterns remains challenging due to the complex interactions among these components. In this study, we analyze spatial patterns associated with patient survival in gastric and colorectal cancer by integrating four predictive classifiers with spatial image statistics across four large patient cohorts. U-Net was used for semantic segmentation of tumor, stroma, and TILs on digitized Hematoxylin and Eosin–stained FFPE whole-slide images, while ResNet-18 was trained to predict Microsatellite Instability (MSI) status. To identify statistically significant tumor hotspot regions, we applied the Getis-Ord Gi* statistic, which evaluates local spatial relationships relative to surrounding tissue regions. Kaplan–Meier survival analyses and log-rank tests were conducted to assess associations between the spatial arrangement of tumor and stroma and overall patient survival. Our findings reveal that the stromal composition surrounding tumor hotspot regions, as delineated by the Getis-Ord Gi* statistic, is significantly associated with differences in overall survival in both gastric and colorectal cancer. Additionally, log-rank tests were used to evaluate the relationship between stromal composition, MSI status, and ACTA2 expression levels.
Included in
GRM-1150 Investigating Spatial Patterns of Tumor and Stroma in Gastric and Colorectal Cancer for Survival Prediction
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
The spatial organization of tumor cells, stroma, and tumor-infiltrating lymphocytes (TILs) within the tumor microenvironment plays a critical role in cancer progression and is strongly associated with clinical outcomes. However, quantifying the significance and statistical impact of these spatial patterns remains challenging due to the complex interactions among these components. In this study, we analyze spatial patterns associated with patient survival in gastric and colorectal cancer by integrating four predictive classifiers with spatial image statistics across four large patient cohorts. U-Net was used for semantic segmentation of tumor, stroma, and TILs on digitized Hematoxylin and Eosin–stained FFPE whole-slide images, while ResNet-18 was trained to predict Microsatellite Instability (MSI) status. To identify statistically significant tumor hotspot regions, we applied the Getis-Ord Gi* statistic, which evaluates local spatial relationships relative to surrounding tissue regions. Kaplan–Meier survival analyses and log-rank tests were conducted to assess associations between the spatial arrangement of tumor and stroma and overall patient survival. Our findings reveal that the stromal composition surrounding tumor hotspot regions, as delineated by the Getis-Ord Gi* statistic, is significantly associated with differences in overall survival in both gastric and colorectal cancer. Additionally, log-rank tests were used to evaluate the relationship between stromal composition, MSI status, and ACTA2 expression levels.