NCI Data Jamboree (Project Abstract Submission): Submission #49

Submission information
Submission Number: 49
Submission ID: 189159
Submission UUID: f608d398-1872-4247-88ec-4d2adc5f10f4

Created: Mon, 07/27/2026 - 15:22
Completed: Mon, 07/27/2026 - 15:39
Changed: Mon, 07/27/2026 - 15:39

Remote IP address: 10.208.28.116
Submitted by: Anonymous
Language: English

Is draft: No
First Name Nikita
Middle Initial
Last Name Thakur
Degree(s) Master of Science, Computer Science
Position/Title/Career Status Research Data Analyst (Analytics and AI lead)
Organization University of Illinois Cancer Center
Organization Address Chicago
Email nthaku3@uic.edu
List of Additional Authors
  • First Name: Saransh
    Last Name: Singh
    Affiliation: University of Illinois Cancer Center
  • First Name: Juhi
    Last Name: Anand
    Affiliation: University of Illinois Cancer Center
  • First Name: Lakshmi Sravya
    Last Name: Rachakonda
    Affiliation: University of Illinois Cancer Center
Abstract Category Evaluating data quality for reproducibility and AI-readiness
Abstract Keywords AI-readiness; data quality; Cancer Research Data Commons; multimodal oncology data; data leakage
Abstract Title AI-Readiness Scorecard: A Task-Relative Profiler for Multimodal Cancer Research Data Commons Datasets
Abstract Rationale: NIH Bridge2AI criteria establish that FAIR conformance alone does not make a dataset AI-ready, yet the criterion most likely to invalidate a downstream model, Characterization, remains largely unautomated. Researchers assembling oncology AI cohorts evaluate batch effects, informative missingness, leakage risk, and clinical-variable completeness ad hoc and per repository, because CRDC Commons were developed independently with distinct data models.
Objective: Prototype an installable Python profiler answering one question: Does this oncology dataset support this AI task, and if not, what is missing?
Approach: Repository adapters reduce heterogeneous inputs to a shared representation of feature matrix, sample metadata, and resolved site label. Two check families run against it. Modality-agnostic integrity checks cover missingness and its correlation with site, class imbalance, batch, and site effects via cross-validated site-predictability under a permutation null, and leakage from patient-straddle or identifier columns. Oncology completeness checks cover staging, histology, treatment, outcomes, actionable biomarkers, and ICD-O/mCODE conformance. Domain and task knowledge live in declarative YAML packs rather than code, keeping the engine small and extensible. Output is an evidence-linked data card plus Croissant metadata.
Datasets: GDC TCGA-BRCA (clinical, expression, mutations) and one IDC imaging collection; PDC CPTAC proteomics and SEER as stretch targets. Open access tiers throughout.
Jamboree deliverables: A public repository containing the intermediate representation and adapter interface, working batch-effect and leakage checks validated on TCGA-BRCA, a first oncology completeness pack, and a per-patient multimodal linkage matrix spanning GDC and IDC, with a rendered data card.
Tools and environment: Python (pandas, scikit-learn, pydicom), open-tier repository APIs, and modest compute: the design is metadata-first and requires no pixel-level processing.
Team: Assembled and ready (see additional authors), four members spanning computer science and healthcare data analytics, with roles mapped to architecture layers: representation, integrity checks, oncology pack, and adapters.