Pathological primary tumor staging depends on contextual evidence across multiple magnifications, but dense annotation on gigapixel whole-slide images is impractical. This work introduces a structure-aware hierarchical graph-based MIL framework that progressively models cross-scale contextual information instead of treating patches independently. The method improves weakly supervised pT staging by capturing multiscale structural cues that are critical for prognosis-related classification.