ArticleCancer biomarkers : section A of Disease markers2025
Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer.
Article in Cancer biomarkers : section A of Disease markers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Cohort-Aware Agents for Individualized Lung Cancer Risk Prediction Using a Retrieval-Augmented Model Selection Framework.Proceedings of SPIE--the International Society for Optical Engineering · 2026Article
- Unsupervised discovery of clinical disease signatures using probabilistic independence.Journal of biomedical informatics · 2025Article
- Longitudinal Masked Representation Learning for Pulmonary Nodule Diagnosis from Language Embedded EHRs.medRxiv : the preprint server for health sciences · 2025Article
- Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules.Radiology. Artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BackgroundLarge community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models.ObjectiveA robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale.MethodsIn this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center.ResultsOur approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs.ConclusionsThis work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.
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