M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling
Bibliographic record
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection critical for improving survival rates, yet conventional methods like CT scans often yield high false-positive rates. This study introduces M-GNN, a graph neural network framework leveraging GraphSAGE, to enhance early lung cancer detection through metabolomics. We constructed a heterogeneous graph integrating metabolomics data from 800 plasma samples (586 cases, 214 controls) with demographic features and Human Metabolome Database annotations, employing GraphSAGE and GAT layers for inductive learning on 107 metabolites, pathways, and diseases. M-GNN achieved a test accuracy of 89% and an ROC-AUC of 0.92, with rapid convergence within 400 epochs and robust performance across ten random seeds; key predictors included age, height, choline, Valine, Betaine, and Fumaric Acid, reflecting smoking and metabolic dysregulation. This framework offers a scalable, interpretable tool for precision oncology, surpassing benchmarks by capturing complex biological interactions, though limitations like synthetic data biases and computational demands suggest future validation with real-world cohorts and optimization. M-GNN advances lung cancer screening, promising improved survival through early detection and personalized strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".