Survival Prediction Across Diverse Cancer Types Using Neural Networks
Xu Yan Weimin Wang MingXuan Xiao Yufeng Li Min Gao
Gastric cancer and Colon adenocarcinoma represent widespread and challenging
malignancies with high mortality rates and complex treatment landscapes. In
response to the critical need for accurate prognosis in cancer patients, the
medical community has embraced the 5-year survival rate as a vital metric for
estimating patient outcomes. This study introduces a pioneering approach to
enhance survival prediction models for gastric and Colon adenocarcinoma
patients. Leveraging advanced image analysis techniques, we sliced whole slide
images (WSI) of these cancers, extracting comprehensive features to capture
nuanced tumor characteristics. Subsequently, we constructed patient-level
graphs, encapsulating intricate spatial relationships within tumor tissues.
These graphs served as inputs for a sophisticated 4-layer graph convolutional
neural network (GCN), designed to exploit the inherent connectivity of the data
for comprehensive analysis and prediction. By integrating patients' total
survival time and survival status, we computed C-index values for gastric
cancer and Colon adenocarcinoma, yielding 0.57 and 0.64, respectively.
Significantly surpassing previous convolutional neural network models, these
results underscore the efficacy of our approach in accurately predicting
patient survival outcomes. This research holds profound implications for both
the medical and AI communities, offering insights into cancer biology and
progression while advancing personalized treatment strategies. Ultimately, our
study represents a significant stride in leveraging AI-driven methodologies to
revolutionize cancer prognosis and improve patient outcomes on a global scale.