RNN-based Prediction and Risk Classifcation for Improving Endometrial Cancer Diagnosis using Clinical and Imaging Data

Endometrial cancer is one of the most prevalent malignancies in women; hence, its early detection is critical for effective treatment and improved survival rates. Traditional techniques for diagnosis are usually invasive and quite variable in their accuracy. This study is primarily focused on applying machine learning, and more specifically recurrent neural networks (RNN), for the analysis of clinical and imaging data for early cancer detection and assessing recurrence risk. Patient datasets were preprocessed through normalization, imputation, and feature extraction. PCA and t-SNE-related dimensionality reduction methods were applied to enhance feature relevance and visualization. The RNNs have a great potential for the analysis of temporal data, but the initial results indicate a poor performance in classification, with an ROC AUC sitting at 0.46. Visualizations suggest overlapping feature spaces, calling for new modeling strategies. Optimizing model architecture, enhancing data varieties, and establishing integration of multimodal sources will be the strategy for establishing clinical utility. The study, while affirming the promise of artificial intelligence in personalized cancer diagnostics, also acknowledges the challenges posed by data quality, model generalization, and interpretability.