TheraRoute-L: Compositional Treatment-Unit Learning for Multimodal Response Prediction across Clinical Settings

Coarse treatment encodings leave drug composition and order implicit in multimodal prediction of pathological complete response (pCR) to neoadjuvant therapy. Learning patient-dependent interactions is further complicated by variable imaging histories and missing inputs. We introduce TheraRoute-L, jointly learning record-supported treatment-unit and relation representations under pCR supervision while retaining whole-regimen semantics. Clinical fields and observed MRI histories condition unit reads and bilinear scores of concurrent or sequential relations before fusion. A causal history encoder updates patient state, and seven private modality-subset experts combine available predictions. We derive interaction and read-sensitivity bounds and pooled and direct-read gradient paths, showing how direct reads bypass the zero-initialized residual map to supervise shared units. Retrospective evaluation spans four cohorts in two countries and multiple regions. With shared patient splits, labels, MRI preprocessing and scoring, baseline AUCs of 71.12% for I-SPY2 + Duke, 83.68% for Hospital-A and 79.56% for Hospital-B exceed those of retrained MRP–MRI and MORM in all three populations. Hospital-B AUC gains are 11.84% and 11.21%, respectively. Frozen expert restrictions support clinical–treatment complementarity.