Block Graph Neural Networks for tumor heterogeneity prediction

• URL: http://arxiv.org/abs/2502.05458v1
• Date: Sat, 08 Feb 2025 05:48:09 GMT
• Title: Block Graph Neural Networks for tumor heterogeneity prediction
• Authors: Marianne Abémgnigni Njifon, Tobias Weber, Viktor Bezborodov, Tyll Krueger, Dominic Schuhmacher,
• Abstract summary: Accurate tumor classification is essential for selecting effective treatments. Standard tumor grading, which categorizes tumors based on cell differentiation, is not recommended as a stand-alone procedure. We propose to build on a mathematical model that simulates tumor evolution and generate artificial datasets for tumor classification.
• Score: 0.3611754783778107
• License:
• 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.

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