ArticleChest2024
A Deep Learning-Based Radiomic Classifier for Usual Interstitial Pneumonia.
Article in Chest, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.International journal of rheumatic diseases · 2026Pooled it
- Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.European journal of medical research · 2025Pooled it
- Automated diagnosis of usual interstitial pneumonia on chest CT via the mean curvature of isophotes.European journal of radiology open · 2026Article
- Review
- e-Lung computed tomography biomarkers are associated with outcomes in fibrotic interstitial lung diseases.ERJ open research · 2026Article
- An interpretable machine learning model integrating clinical and CT radiomic features for the detection of interstitial lung disease in idiopathic inflammatory myopathies.Arthritis research & therapy · 2026Article
- Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions.The European respiratory journal · 2026Review
- LPC-transformer: a training strategy optimized framework for multiclass pneumonia medical image classification.Frontiers in medicine · 2026Article
- Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification.European radiology · 2025Article
- Pulmonary Fibrosis-Focusing on the Future: Aspen Lung Conference 2024 Summary.American journal of respiratory cell and molecular biology · 2025Article
- Quantitative Computed Tomography Measures of Lung Fibrosis and Outcomes in the National Lung Screening Trial.Annals of the American Thoracic Society · 2025Article
- Diagnosis of early idiopathic pulmonary fibrosis: current status and future perspective.Respiratory research · 2025Review
- Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2025Review
- SqueezeViX-Net with SOAE: A Prevailing Deep Learning Framework for Accurate Pneumonia Classification using X-Ray and CT Imaging Modalities.Current medical imaging · 2025Article
- Response.Chest · 2024Article
Corrections and comments
- Commented on by
Authors and funding
17 authors.
Funding
Abstract
backgroundBecause chest CT scan has largely supplanted surgical lung biopsy for diagnosing most cases of interstitial lung disease (ILD), tools to standardize CT scan interpretation are urgently needed. RESEARCH QUESTION: Does a deep learning (DL)-based classifier for usual interstitial pneumonia (UIP) derived using CT scan features accurately discriminate radiologist-determined visual UIP? STUDY DESIGN AND
methodsA retrospective cohort study was performed. Chest CT scans acquired in individuals with and without ILD were drawn from a variety of public and private data sources. Using radiologist-determined visual UIP as ground truth, a convolutional neural network was used to learn discrete CT scan features of UIP, with outputs used to predict the likelihood of UIP using a linear support vector machine. Test performance characteristics were assessed in an independent performance cohort and multicenter ILD clinical cohort. Transplant-free survival was compared between UIP classification approaches using the Kaplan-Meier estimator and Cox proportional hazards regression.
resultsA total of 2,907 chest CT scans were included in the training (n = 1,934), validation (n = 408), and performance (n = 565) data sets. The prevalence of radiologist-determined visual UIP was 12.4% and 37.1% in the performance and ILD clinical cohorts, respectively. The DL-based UIP classifier predicted visual UIP in the performance cohort with sensitivity and specificity of 93% and 86%, respectively, and in the multicenter ILD clinical cohort with 81% and 77%, respectively. DL-based and visual UIP classification similarly discriminated survival, and outcomes were consistent among cases with positive DL-based UIP classification irrespective of visual classification.
interpretationA DL-based classifier for UIP demonstrated good test performance across a wide range of UIP prevalence and similarly discriminated survival when compared with radiologist-determined UIP. This automated tool could efficiently screen for UIP in patients undergoing chest CT scan and identify a high-risk phenotype among those with known ILD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.