Journal Article

CAVE-Onc: Graph-Constrained Agentic Validation for Cross-Domain Contradictions in CDISC Oncology Submissions

Jaime Yan

PLOS One · 2026 · sole author

Abstract

Regulatory submissions in oncology must remain consistent across interdependent CDISC SDTM domains, yet cross-domain contradictions routinely survive conventional validation. CAVE-Onc introduces a graph-constrained agentic validation framework that models SDTM submission datasets as an RDF knowledge graph, combining declarative SHACL-SPARQL graph constraints with a deterministic LangGraph-based agent layer to detect cross-domain contradictions that domain-scoped rule-based validators cannot express. In a pre-registered evaluation, CAVE-Onc detected all 20 clinician-reviewed injected contradiction archetypes (vs 8/20 for the CDISC CORE engine and 6/20 for the Pinnacle 21 FDA engine, both 0/10 on cross-domain RECIST contradictions) — a construction validation of expressiveness — and stayed specific (0.06–0.09 flags/subject) on two real Project Data Sphere oncology trials mapped to SDTM, detecting 10/11 and 16/18 of applicable injected archetypes. CAVE-Onc writes all validation traces to a Merkle-chained, tamper-evident audit store, providing a foundation for 21 CFR Part 11 compliance.

Keywords

CDISC · SDTM · SHACL · SPARQL · RDF knowledge graph · RECIST 1.1 · oncology

Cite

Jaime Yan. "CAVE-Onc: Graph-Constrained Agentic Validation for Cross-Domain Contradictions in CDISC Oncology Submissions." PLOS One, 2026. doi:10.1371/journal.pone.0350376.