Multi-Timepoint Risk Stratification in Rare Cancers: A Computational Framework Validated against Published Ewing Sarcoma Trial Data

Three audiences -- the family of a newly diagnosed Ewing sarcoma patient, the long-term survivor, and the cooperative-group trial statistician -- receive cohort-mean answers to patient-level questions because the patient-level data machine learning requires do not exist for rare cancers. We present a framework producing patient-level predictions from published aggregate trial data. A six-stage discrete-event Monte Carlo simulation integrates genetic risk factors, serial biomarker dynamics with genotype-conditional weighting, post-surgical ctDNA-based minimal residual disease (ctDNA-MRD) assessment, and treatment-related mortality as a separable competing risk. Adverse-effects modules project 30-year incidence across five organ systems from chemotherapy and radiation exposures. Its four structural ingredients are instantiated in Ewing sarcoma and validated against trial data from more than 3,400 patients. The framework achieves 3.2% mean absolute error across 23 efficacy endpoints (none exceeding 6%) and falls within published confidence intervals for all 20 toxicity endpoints. ctDNA-MRD stratification separates candidate populations -- 5.5% recurrence (de-escalation) versus 87.8% (intensification) -- and multi-timepoint integration produces 16-fold five-year EFS resolution spanning 5-96%, exceeding the 3- to 5-fold ranges of single-timepoint approaches. The 16.1-fold recurrence risk ratio emerges from simulation, not as a supplied parameter. Genotype-conditional weighting improves discrimination over equal-weight scoring in every subgroup (Pearson r +0.060 to +0.129), with largest gains where biological rationale is strongest. A Monte Carlo framework calibrated to published aggregate data turns cohort-mean answers into patient-level predictions as exemplified in the rare cancer Ewing sarcoma, where the conventional patient-level machine-learning pathway is structurally unavailable; transfer to other rare cancers remains a hypothesis for future validation. Survivorship-surveillance refinement is the most concrete current use; trial-design and prognostic counseling are next-decade pathways.