Algorithms for Growth Pattern Based Analysis of Lung Adenocarcinoma Histology

Abstract

Lung Adenocarcinoma (LUAD) is a leading cause of cancer deaths and shows considerable variation in patient outcomes due to its complex histological, immune, and molecular diversity. Traditional computational pathology methods use global, pattern-agnostic representations that do not capture the structured organization of tumor tissue. This thesis develops new, pattern-aware computational approaches to address this gap. First, we introduce CellOMaps, a biologically informed image representation that encodes the cellular organization of LUAD tissue. By focusing on spatial structure rather than raw visual appearance, CellOMaps enables robust, scalable characterization of LUAD histological growth patterns. Using this representation, we propose a framework for dense, patch-level growth pattern classification across Whole Slide Images (WSIs), offering a reproducible and spatially resolved alternative to conventional predominant-pattern assessment and demonstrating improved robustness across institutions. Next, we investigate immune heterogeneity in LUAD by analyzing the spatial distribution of Tumor-Infiltrating Lymphocytes (TILs). We introduce GPS-TILs, a growth pattern–specific digital biomarker that quantifies the presence, density, abundance, and spatial dispersion of immune infiltration within distinct morphological patterns. This pattern-aware immune profiling improves prognostic stratification compared to global TILs assessment and manual grading, highlighting the importance of incorporating histological context into immune analysis. Finally, we address molecular inference from histopathology by predicting Tumor Mutational Burden (TMB) from WSIs. We introduce GRIL-GNN, a graph-based learning framework that aggregates information across spatially organized tissue regions while preserving detailed morphological features. This approach uses a novel unified learning objective that combines supervised learning with self-supervised regularization, enabling robust representation learning under weak supervision. By restricting analysis to localized tissue regions with shared histological patterns, this work examines the distribution of the TMB predictive signal within different morphological patterns. Overall, this work demonstrates that integrating spatial and pattern-aware analysis improves the interpretation of morphological, immune, and molecular signals in LUAD. The approaches proposed here provide a framework for more precise, clinically relevant computational pathology, with potential applications to other cancers characterized by complex tissue structure.