Real-world EHR-derived progression-free survival across successive lines of therapy informs metastatic breast cancer risk stratification

Abstract Guideline-based recommendations for metastatic lines of therapy (mLoTs), especially second lines and beyond, are comparatively sparse due to challenges in later-line treatment efficacy quantification. Scalable real-world evidence that captures the interaction between treatment and disease progression is therefore especially valuable, as regimens become increasingly individualized, confounding intensifies, and progression is rarely recorded as a structured EHR endpoint. We present a framework to (i) reconstruct clinically coherent mLoTs from longitudinal EHR using radiology-anchored progression evidence and (ii) generate individualized progression-free survival (PFS) estimates from a line-start multi-modal snapshot in a highly heterogeneous cohort. In 2,881 patients contributing 8,791 metastatic mLoTs, the selected model shows good discrimination over a 2-year horizon (Antolini’s C = 0.681 ± 0.006; cumulative/dynamic AUC at 1 year = 0.824 ± 0.008). Predicted risk strata closely track Kaplan–Meier trends across line number and tumor subtypes, supporting risk stratification with similar calibration across sub-cohorts. Model prediction primarily relies on clinically plausible signals of recent metastatic burden and tumor markers, with limited dependence on surveillance cadence or subtype labels, and is robust to modality ablation. Together, this framework supports scalable evidence generation for metastatic line PFS patterns and interpretable, broadly calibrated prognostication to inform risk assessment and care planning in heterogeneous metastatic practice.