G3I-Net: A Trimodal Architecture for Tumor Staging and How Much Its Gene Graphs Actually Vary

Architectures that condition one modality's structure on another are evaluated by end-task accuracy. That measurement cannot separate a mechanism producing genuinely input-dependent structure from one that has collapsed onto a single solution shared by every input. We build such a mechanism and measure it directly. G3I-Net is a trimodal architecture for non-small-cell lung cancer T-staging: a frozen Swin-UNETR encodes CT volumes, an MLP encodes radiomic descriptors, and a graph neural network encodes pathway genes over a protein–protein interaction prior. A hypernetwork reads the CT embedding and emits one score per gene, from which a per-patient adjacency over that prior is built, so the molecular branch computes over a topology derived from the patient's own tumor rather than one shared across the cohort. On 128 patients with 21 advanced-stage events the model reaches a patient-level AUC of 0.9119, against 0.749 for the strongest classical late-fusion baseline. We then measure the generated graphs. Each sits far from the prior it was built from, so the conditioning is not inert, yet the graphs are nearly identical to one another: the architecture allows sixteen independent ways for patients to differ and the trained model uses about one. Widening the hypernetwork's input leaves that unchanged, and letting it score every edge freely lowers it further. Removing the conditioning entirely leaves every patient's predicted class unchanged, while two implementations of the same model differing only in the order of floating-point operations change two. What limits conditioned graph generation here is not how much the architecture can express, but how much of it the trained model uses.