Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy
Abstract
Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
RTI risk is shaped not only by clinical factors but also by social and environmental determinants that are rarely captured in traditional registry data, including transportation barriers, housing instability, and neighborhood-level deprivation.
“RTI risk is shaped not only by clinical factors but also by social and environmental determinants that are rarely captured in traditional registry data, including transportation barriers, housing instability, and neighborhood-level deprivation.20”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [20]
The Abstract links social disadvantage with increased radiation therapy interruptions, and the Methods describe combining community-level social determinants with patient and clinical data. The paper does not identify transportation barriers, housing instability, or neighborhood-level deprivation as specific risks, nor does it say these factors are rarely captured in traditional registry data. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 4
- Hubler A, Wakefield DV, Makepeace L, Carnell M, Sharma AM, Jiang B, Dove AP, Garner WB, Edmonston D, Little JG, Ozdenerol E, Hanson RB, Martin MY, Shaban-Nejad A, Pisu M, Schwartz DL. Independent Predictors for Hospitalization-Associated Radiation therapy Interruptions. Adv Radiat Oncol. 2022 Jul;7(6):101041, doi: 10.1016/j.adro.2022.101041. · DOI
- Ammar N, Bailey JE, Davis RL, Shaban-Nejad A. Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data. JMIR Form Res. 2021 Mar;5(3):e24738, doi: 10.2196/24738. · DOI · Paper page
- Brakefield WS, Ammar N, Shaban-Nejad A. An Urban Population Health Observatory for Disease Causal Pathway Analysis and Decision Support: Underlying Explainable Artificial Intelligence Model. JMIR Form Res. 2022 Jul;6(7):e36055, doi: 10.2196/36055. · DOI
- Schwartz DL, Stewart A, Harris L, Ozdenerol E, Thomas F, Johnson KC, Davis R, Shaban-Nejad A. The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance. Disaster Med Public Health Prep. 2022 Dec;17:e326, doi: 10.1017/dmp.2022.284. · DOI
Cited by
Sorted by citations
Zink, J. A., Rashid, R., Chinthala, L., Kheirinejad, S., White, B. M., Kheirkhah Rahimabad, P., Hashtarkhani, S., Kumsa, F. A., Brett, C. L., Davis, R. L., et al. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100771 · Cited by 1 · see citing works
Self-citation
- Authors
- Shaban-Nejad, A., Ammar, N., Kumsa, F., Hashtarkhani, S., White, B., Chinthala, L. K., Owens, C. A., Hayes, N., & Schwartz, D. L.
- Affiliations
- University of Tennessee Health Science Center – Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA
- School of Information Technology, College of Applied Science and Technology, Illinois State University, Normal, Illinois, USA
- University of Tennessee Health Science Center, Memphis, Tennessee, USA
- Division of Med Hematology, Department of Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA
- Departments of Radiation Oncology & Preventive Medicine University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA
- ORCID
- Shaban-Nejad, Arash
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2024-01-25
- ISSN
- 0926-9630
- 1879-8365
- Cited by
- 1
- References
- 0
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
