OncoTwin finds the most similar real-world patients ("twins") using clinico-genomic similarity and network algorithms—then summarizes treatment journeys and observed outcomes with transparent rationale.
Weighted similarity across clinical context, biomarkers, and genomics—with auditable drivers of match and confidence bands.
Graph methods stabilize retrieval and reduce one-off coincidences—surfacing clusters of related cases, not just a single nearest neighbor.
Cohort-level rates among similar patients (with n): durability, early failure, and outcome context by treatment class.
Standardize clinical + biomarker + genomic representations.
Stage and driver cohort alignment to avoid implausible comparisons.
Tuned weights + subcohort logic + co-alteration penalties.
Top twins + match drivers + peer evidence panel (n, confidence).
Experience the future of evidence-backed oncology. OncoTwin helps oncologists move beyond static reports by matching each patient’s genomic and clinical profile to thousands of global outcomes—delivering evidence-backed treatment options in real time.
A structured, analytically rigorous patient similarity report designed for clinical interpretability and reproducibility.
Top-matched patient twins with quantified similarity scores and contributing match features
Comparative treatment trajectories, including lines of therapy, treatment duration, and progression-related signals
Peer evidence summary, presenting outcome rates with corresponding cohort size and statistical confidence indicators
Risk and limitation annotations, highlighting data sparsity, potential confounding factors, and completeness considerations
A structured, data-integrated workspace designed to support multidisciplinary molecular tumour board discussions through unified, evidence-contextualized case review.
Integrated case view consolidating genomic findings, clinical history, biomarkers, and prior treatments in a single standardized format
Evidence contextualization layer linking molecular alterations to guidelines, literature, and real-world cohort signals
Twin-based cohort insights providing outcome context from clinically similar patients without prescriptive inference
Decision support, not a clinical directive
Observational evidence (not guaranteed outcomes; not causal claims)
Transparent rationale for every match
Confidence-aware: small samples are flagged