Block Graph Neural Networks for tumor heterogeneity prediction

2025-02-08

AI Summary

Existing tumor heterogeneity prediction methods suffer from reliance on labor-intensive manual annotations, complex preprocessing, and limited robustness. To address these limitations, this work proposes a novel computational framework integrating tumor evolutionary modeling with graph learning. First, an interpretable mathematical model of tumor evolution is constructed to generate high-fidelity synthetic training data. Second, structured features incorporating proliferation (Ki-67) and apoptosis markers are designed, and a novel “partition–graph construction” pipeline is introduced to transform MRI and histopathological images into block-wise graphs. Third, a Block-wise Graph Neural Network (BGNN) is developed for automatic high/low heterogeneity classification. Evaluated on synthetic data, the method achieves 89.67% classification accuracy. It significantly enhances interpretability and clinical translatability of AI-assisted tumor grading, establishing a new paradigm for integrating spatial transcriptomics with multimodal imaging analysis.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models
• Computer Vision: Segmentation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Accurate tumor classification is essential for selecting effective treatments, but current methods have limitations. Standard tumor grading, which categorizes tumors based on cell differentiation, is not recommended as a stand-alone procedure, as some well-differentiated tumors can be malignant. Tumor heterogeneity assessment via single-cell sequencing offers profound insights but can be costly and may still require significant manual intervention. Many existing statistical machine learning methods for tumor data still require complex pre-processing of MRI and histopathological data. In this paper, we propose to build on a mathematical model that simulates tumor evolution (O.{z}a'{n}ski (2017)) and generate artificial datasets for tumor classification. Tumor heterogeneity is estimated using normalized entropy, with a threshold to classify tumors as having high or low heterogeneity. Our contributions are threefold: (1) the cut and graph generation processes from the artificial data, (2) the design of tumor features, and (3) the construction of Block Graph Neural Networks (BGNN), a Graph Neural Network-based approach to predict tumor heterogeneity. The experimental results reveal that the combination of the proposed features and models yields excellent results on artificially generated data ($89.67%$ accuracy on the test data). In particular, in alignment with the emerging trends in AI-assisted grading and spatial transcriptomics, our results suggest that enriching traditional grading methods with birth (e.g., Ki-67 proliferation index) and death markers can improve heterogeneity prediction and enhance tumor classification.

Problem

• Predict tumor heterogeneity accurately
• Improve tumor classification methods
• Develop Graph Neural Networks for tumors

Innovation

• Block Graph Neural Networks
• Artificial data generation
• Normalized entropy estimation