Exploratory associations between radiographic findings and metadata-derived proxies of 90-day follow-up in 112,120 ChestX-ray14 radiographs

Chest radiography is widely used as an initial imaging modality. However, how specific findings relate to subsequent care or follow-up actions remains unclear. Prior studies have rarely examined follow-up actions, and potential sex-specific differences have been understudied. We analyzed 112,120 frontal chest radiographs from the NIH ChestX-ray14 dataset (63,340 male, 48,780 female). Images were labeled with 14 findings using a natural language processing (NLP) pipeline applied to reports. We modeled a metadata-derived proxy of 90-day follow-up using logistic regression, including sex-stratified analyses and interaction testing, with models adjusted for sex. Robustness was assessed through sensitivity analyses (30/60/180-day windows), patient-level clustering, and false discovery rate (FDR) adjustment. The strongest associations with proxy follow-up were observed for pulmonary edema (OR 10.6, 95% CI 8.5-13.2), pneumothorax (OR 7.6, 95% CI 6.7-8.6), and pleural effusion (OR 4.0, 95% CI 3.8-4.3). Interactions between sex and specific findings were modest but reached statistical significance for atelectasis (P = 0.003), pneumothorax (P = 0.0083), and emphysema (P = 0.0238). Radiographic findings were associated with metadata-derived proxy follow-up, but residual confounding from unmeasured factors (e.g., age, comorbidities, clinical context) likely remains. Sex-specific effects were statistically significant but small, and not clinically meaningful. Results should therefore be interpreted strictly as hypothesis-generating signals, not as causal evidence or clinically directive guidance.

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Journal
Scientific Reports
Published
2025-12-09
DOI
https://doi.org/10.1038/s41598-025-31885-3
Primary Topic
COVID-19 diagnosis using AI
Type
article
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article

Exploratory associations between radiographic findings and metadata-derived proxies of 90-day follow-up in 112,120 ChestX-ray14 radiographs

Christian Biancosino, Kurt Rasche, M. Krüger, Josef Yayan
Scientific Reports
COVID-19 diagnosis using AI
article

Exploratory associations between radiographic findings and metadata-derived proxies of 90-day follow-up in 112,120 ChestX-ray14 radiographs

Christian Biancosino, Kurt Rasche, M. Krüger, Josef Yayan
article en

Abstract

Chest radiography is widely used as an initial imaging modality. However, how specific findings relate to subsequent care or follow-up actions remains unclear. Prior studies have rarely examined follow-up actions, and potential sex-specific differences have been understudied. We analyzed 112,120 frontal chest radiographs from the NIH ChestX-ray14 dataset (63,340 male, 48,780 female). Images were labeled with 14 findings using a natural language processing (NLP) pipeline applied to reports. We modeled a metadata-derived proxy of 90-day follow-up using logistic regression, including sex-stratified analyses and interaction testing, with models adjusted for sex. Robustness was assessed through sensitivity analyses (30/60/180-day windows), patient-level clustering, and false discovery rate (FDR) adjustment. The strongest associations with proxy follow-up were observed for pulmonary edema (OR 10.6, 95% CI 8.5-13.2), pneumothorax (OR 7.6, 95% CI 6.7-8.6), and pleural effusion (OR 4.0, 95% CI 3.8-4.3). Interactions between sex and specific findings were modest but reached statistical significance for atelectasis (P = 0.003), pneumothorax (P = 0.0083), and emphysema (P = 0.0238). Radiographic findings were associated with metadata-derived proxy follow-up, but residual confounding from unmeasured factors (e.g., age, comorbidities, clinical context) likely remains. Sex-specific effects were statistically significant but small, and not clinically meaningful. Results should therefore be interpreted strictly as hypothesis-generating signals, not as causal evidence or clinically directive guidance.

Scientific ReportsVol. 15(1)
Witten/Herdecke University (DE), Helios Universitätsklinikum Wuppertal (DE), Martha-Maria Hospital (DE)
Openalex Percentile: Top 46%
COVID-19 diagnosis using AI
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