Longitudinal Medical World Models for Patient-Specific Disease Dynamics

Patients with brain tumours are followed with serial MRI at irregular intervals, and each scan informs whether treatment continues or changes. Anticipating how the tumour will evolve before the next scan could support these decisions. This paper shows that such forecasts are best built from disease dynamics shared across patients, adapted to each patient’s treatment and history. We present Longitudi- nal Medical World Models (LMWM). LMWM learns one latent transition from many patients and adapts it with two additive signals: a treatment response field that encodes where and when each recorded course acts, and a history map of the deviations that recur in the patient’s own past forecasts. Without these signals, LMWM is exactly the shared model, so the effect of treatment and history can be measured directly. Evaluating such forecasts also needs care: image similar- ity favours unchanged predictions, and copying the current scan already attains a follow-up lesion Dice of 72.9%. We therefore score the predicted change with a signed change F1 (cF1). On patient timelines built from twelve public brain tu- mour collections, which align serial MRI with treatment records, LMWM raises cF1 from 31.6% for the shared model to 45.1%, above twelve image-generation baselines, and is the only method whose lesion overlap exceeds that of copying the input. These results point to a simple view of patient-specific forecasting: learn disease dynamics once across patients, adapt them to each individual, and judge the forecast by the change it predicts.