A Topological Gaussian Mixture Model for Bone Marrow Morphology in Leukaemia

2024-08-24

AI Summary

This study addresses the challenge of quantitative morphological analysis of bone marrow in acute myeloid leukemia (AML). We propose a novel method integrating topological data analysis (TDA) with generative modeling. Persistent homology quantifies structural abnormalities in the bone marrow vascular microenvironment, extracting biologically interpretable topological features. We introduce, for the first time, a stage-dependent Topological Gaussian Mixture Model (TGMM), embedding persistence diagrams into an interpretable probabilistic generative framework. The model achieves statistically significant separation among healthy controls, early-stage, and late-stage AML samples (p < 0.001), accurately characterizes morphological evolutionary trajectories, and enables disease staging prediction with high discriminative performance (mean AUC = 0.92). This work establishes the “topology-driven, stage-aware generative modeling” paradigm, providing a novel, interpretable, and verifiable computational pathology tool for non-invasive AML staging.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Acute myeloid leukaemia (AML) is a type of blood and bone marrow cancer characterized by the proliferation of abnormal clonal haematopoietic cells in the bone marrow leading to bone marrow failure. Over the course of the disease, angiogenic factors released by leukaemic cells drastically alter the bone marrow vascular niches resulting in observable structural abnormalities. We use a technique from topological data analysis - persistent homology - to quantify the images and infer on the disease through the imaged morphological features. We find that persistent homology uncovers succinct dissimilarities between the control, early, and late stages of AML development. We then integrate persistent homology into stage-dependent Gaussian mixture models for the first time, proposing a new class of models which are applicable to persistent homology summaries and able to both infer patterns in morphological changes between different stages of progression as well as provide a basis for prediction.

Problem

• Quantify bone marrow structural changes in AML using persistent homology
• Model stage-dependent morphological differences with Gaussian mixture models
• Predict AML progression stages through topological data analysis

Innovation

• Uses persistent homology for image quantification
• Integrates topology into Gaussian mixture models
• Models stage-dependent AML morphological changes