Evidence map›Paper›PMID 42649990›Full record

ArticleCancers2026

Environmental Exposure Context and Advanced Stage at Diagnosis in Thyroid Cancer: A Population-Based SEER Analysis.

Jingjing Tong, Sarat Chandra Gupta Surampalli, Srinidhi Santhosh Kumar, Naga Venkata Sriram Golli

Abstract read
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Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jingjing TongDepartment of Data Analytics and Statistics, University of North Texas, Denton, TX 76203, USA.
Sarat Chandra Gupta SurampalliDepartment of Data Analytics and Statistics, University of North Texas, Denton, TX 76203, USA.
Srinidhi Santhosh KumarDepartment of Data Analytics and Statistics, University of North Texas, Denton, TX 76203, USA.
Naga Venkata Sriram GolliDepartment of Data Analytics and Statistics, University of North Texas, Denton, TX 76203, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEnvironmental exposures are biologically plausible contributors to thyroid cancer outcomes, but population-based evidence is heterogeneous and often focuses on incidence rather than stage at diagnosis.

objectiveThis study aimed to evaluate whether ecological state-year environmental indicators add predictive information for advanced-stage thyroid cancer at diagnosis in a large SEER-based cohort (U.S. National Cancer Institute's Surveillance, Epidemiology, and End Results Program).

methodsWe conducted a retrospective registry-linked analysis of 368,726 SEER thyroid cancer cases with valid combined summary stage. Advanced stage was defined as regional or distant disease and occurred in 118,509 cases (32.14%). Environmental variables were linked as state-year indicators using five-year moving averages from the years preceding diagnosis. Regularized logistic regression, gradient boosting, and Extra Trees models evaluated incremental predictive performance.

resultsAdding environmental variables to demographic, socioeconomic, histology, and laterality predictors produced small random-split improvements across model families. However, after SEER registry was added before environmental variables, the remaining environmental increment was minimal. Registry-group holdout validation did not support improved geographic generalizability from the environmental feature set.

conclusionsState-year environmental indicators carried limited predictive information for advanced thyroid cancer stage, and much of this information overlapped with registry and geographic structure. These findings clarify both the potential and limitations of ecological environmental linkage in SEER-based prediction studies.

Indexed as

advanced stageendocrine disruptorsenvironmental exposurefeature importancemachine learningSEERthyroid cancer

Identifiers

PMID42649990
PMCPMC13510960

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.