Deep Texture Analysis for Automated TNM Staging

The potential of Convolutional Neural Networks (CNNs) in detecting various forms of cancer has been widely recognized. However, the generalizability of metabolic signature-based results across the three categories of TNM staging using deep texture analysis remains largely unexplored. This study seeks to examine the cross-TNM category and cross-cancer relevance of deep textures identified by a 3D CNN model. Our methodology involves processing patches of PET data from various glycolytic volumes, including primary tumor, malignant nodes and metastases, from PETFDG data, with high Standardized Uptake Values (SUV) for patients presenting with clinically confirmed esophageal cancer. Activation values were calculated for each filter in the CNN, and a Principal Component Analysis (PCA) is conducted to assess the model’s ability to generalize its learned metabolic signatures. Our results indicate that a CNN trained for esophageal cancer primary tumor detection can successfully demonstrate distinct patterns of cellular metabolism for primary tumor, malignant nodes and metastases that a CNN trained for esophageal cancer primary tumor detection can discern all parts of esophageal TNM staging categories.