ArticleFrontiers in medicine2022
Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.European journal of medical research · 2025Pooled it
- Nomogram models integrating multiregion radiomics, quantitative computed tomography, and clinical indicators for distinguishing connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis and improving the prediction of gender-age-physiology stage.Quantitative imaging in medicine and surgery · 2026Article
- CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study.iScience · 2026Article
- Associations between CT radiomics analyses and kidney biopsy in patients with kidney disease.BMC nephrology · 2026Article
- Development of imaging protocol and radiomics-based nomogram for assessing lesion reversibility in connective tissue disease-associated interstitial lung disease.BMC medical imaging · 2026Article
- Artificial intelligence in idiopathic pulmonary fibrosis: advances, challenges and future directions.The European respiratory journal · 2026Review
- Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2025Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- Harnessing Radiomics and Explainable AI for the Classification of Usual and Nonspecific Interstitial Pneumonia.Journal of clinical medicine · 2025Article
- Review
- Diagnosis of early idiopathic pulmonary fibrosis: current status and future perspective.Respiratory research · 2025Review
- Computer-Aided Evaluation of Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.Journal of cancer research and clinical oncology · 2025Article
- From images to clinical insights: an educational review on radiomics in lung diseases.Breathe (Sheffield, England) · 2025Review
- Histopathological correlations of CT-based radiomics imaging biomarkers in native kidney biopsy.BMC medical imaging · 2024Article
- Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.Quantitative imaging in medicine and surgery · 2024Article
- Imaging at the nexus: how state of the art imaging techniques can enhance our understanding of cancer and fibrosis.Journal of translational medicine · 2024Review
- The Unveiled Triad: Clinical, Radiological and Pathological Insights into Hypersensitivity Pneumonitis.Journal of clinical medicine · 2024Review
- Article
- Machine Learning Model Integrating CT Radiomics of the Lung to Predict Checkpoint Inhibitor Pneumonitis in Patients with Advanced Cancer.Technology in cancer research & treatmentArticle
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans. Material and Methods: In this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual Results: In five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% ( Conclusion: Deep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.
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