ArticleJapanese journal of radiology2024
Correlation of CT-based radiomics analysis with pathological cellular infiltration in fibrosing interstitial lung diseases.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Toward a standardized and interoperable imaging biomarker catalog.Insights into imaging · 2026Article
- Artificial intelligence and radiomics in drug-induced interstitial lung disease.ERJ open research · 2026Review
- 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
- CT findings of lung injury during breast cancer treatment.Breast cancer (Tokyo, Japan) · 2026Review
- Unlocking the potential of radiomics in identifying fibrosing and inflammatory patterns in interstitial lung disease.La Radiologia medica · 2025Article
- Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2025Review
- Imaging Findings of Pediatric Rheumatic Disorders: JIA in the PRINTO Era and Autoimmune Interferonopathies.Pediatrics international : official journal of the Japan Pediatric SocietyReview
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12 authors.
Funding
Abstract
purposeWe aimed to identify computed tomography (CT) radiomics features that are associated with cellular infiltration and construct CT radiomics models predictive of cellular infiltration in patients with fibrotic ILD. MATERIALS AND
methodsCT images of patients with ILD who underwent surgical lung biopsy (SLB) were analyzed. Radiomics features were extracted using artificial intelligence-based software and PyRadiomics. We constructed a model predicting cell counts in histological specimens, and another model predicting two classifications of higher or lower cellularity. We tested these models using external validation.
resultsOverall, 100 patients (mean age: 62 ± 8.9 [standard deviation] years; 61 men) were included. The CT radiomics model used to predict cell count in 140 histological specimens predicted the actual cell count in 59 external validation specimens (root-mean-square error: 0.797). The two-classification model's accuracy was 70% and the F1 score was 0.73 in the external validation dataset including 30 patients.
conclusionThe CT radiomics-based model developed in this study provided useful information regarding the cellular infiltration in the ILD with good correlation with SLB specimens.
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