CAVE-Onc: Graph-constrained agentic validation for cross-domain contradictions in CDISC oncology submissions
Jaime Yan
Abstract
The CDISC Rules Engine (CORE) provides industry-standard validation for Study Data Tabulation Model (SDTM) submissions, yet its imperative, domain-scoped architecture cannot express cross-domain contradictions—such as a Response Evaluation Criteria in Solid Tumors (RECIST) overall response that contradicts the combined target lesion, non-target lesion, and new-lesion status—which pass structural validators undetected. We present CAVE-Onc, a two-layer graph-constrained agentic validation engine that augments CORE with declarative Shapes Constraint Language (SHACL) validation (L1) and a LangGraph-based agentic orchestrator
Introduction
Clinical trial data submitted to regulatory authorities must conform to the Study Data Tabulation Model (SDTM) standards maintained by the Clinical Data Interchange Standards Consortium (CDISC) . The CDISC Rules Engine (CORE) provides an open-source, imperative validation framework that evaluates domain-scoped rules (field lengths, controlled terminology, required variables) encoded in YAML/JSON definitions sourced from the CDISC Library.
Materials and methods
Ethics statement
This study analyzed only publicly available, de-identified and synthetic datasets—the CDISC SDTM/ADaM Pilot Project data and the open-source pharmaversesdtm package—and did not involve human participants, identifiable patient data, or the collection of new data. Accordingly, institutional review board (IRB) approval and informed consent were not applicable to this work.
Contradiction injection corpus
Twenty contradiction archetypes were enumerated with clinical review (Gate B) covering nine domains (RS, TR, TU, DM, EX, AE, DS, TA, SUPPDM); the full per-archetype catalog is in S1 Table in S1 File. By provenance the archetypes fall into two groups: ten (A08–A17) were derived during the Gate A gap analysis from CORE’s own conformance-rule corpus—each corresponds to an existing CORE rule that CAVE re-expresses as a cross-domain shape—while the other ten
Result
On the 20-archetype injected corpus of RECIST-enriched pharmaversesdtm data, CAVE detected 20/20 archetypes (100%; exact binomial 95% CI : 83.2%–100.0%) . This count is reproducible from the released shapes under the same subject-specific criterion used for the held-out and real-data studies—each archetype’s own shape (or, for A19, the L3 agent) fires on the injected subject, and the two cross-subject/structural archetypes (A16, A17) fire cohort-wide—so it does not depend on a global flag-count delta. We ran the CDISC Rules Engine (CORE v0.15)
Conclusion
CAVE-Onc shows that graph-constrained validation can close a clinically important expressiveness gap in oncology SDTM review. Across a pre-registered two-track evaluation, CAVE-Onc remained complementary to CORE on clean reference data and detected all 20 clinician-reviewed contradiction archetypes in the injected corpus—a construction validation of the architecture’s expressiveness rather than an estimate of real-world detection—including one RECIST
Article information
Citation: Yan J (2026) CAVE-Onc: Graph-constrained agentic validation for cross-domain contradictions in CDISC oncology submissions. PLoS One 21(8): e0350376. https://doi.org/10.1371/journal.pone.0350376
Editor: Le Zhang, Sichuan University, CHINA
Received: May 11, 2026; Accepted: July 30, 2026; Published: August 14, 2026
Copyright: © 2026 Jaime Yan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All code, SHACL shapes, evaluation scripts, and synthetic benchmark data are publicly available without restriction in the CAVE-Onc-Benchmark repository (GitHub: yanmingyu92/CAVE-Onc-Benchmark), https://github.com/yanmingyu92/CAVE-Onc-Benchmark. All datasets used in this study are open-source or synthetic (pharmaversesdtm, CDISC SDTM/ADaM Pilot Project). The de-identified per-archetype expert-rating matrix underlying the inter-rater agreement statistic is included in the repository (eval/expert_ratings_deidentified.csv), together with a reproduction script (scripts/reproduce_kappa.py); no reviewer-identifying information is shared. The OSF pre-registration protocol is available in the OSF project osf.io/fx2ky, https://osf.io/fx2ky.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
