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Rib suppression-based radiomics for diagnosis of neonatal respiratory distress syndrome in chest X-rays.

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  • Additional Information
    • Source:
      Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
    • Publication Information:
      Original Publication: London : Nature Publishing Group, copyright 2011-
    • Subject Terms:
      Respiratory Distress Syndrome, Newborn*/diagnostic imaging ; Respiratory Distress Syndrome, Newborn*/diagnosis ; Ribs*/diagnostic imaging ; Radiography, Thoracic*/methods; Humans ; Infant, Newborn ; Female ; Male ; ROC Curve ; Machine Learning ; Area Under Curve ; Radiomics
    • Abstract:
      This study aims to refine a radiomics-based diagnostic approach for detecting neonatal respiratory distress syndrome (NRDS) and examines the influence of rib suppression on the diagnostic precision of radiomics models using neonatal chest X-ray (CXR) images. A total of 138 CXR images were collected in this study. The data was partitioned into training and validation subsets based on chronological order. We applied rib suppression to the CXR images and extracted and analyzed radiomic features from lung regions both before and after rib suppression. This approach was designed to identify NRDS, develop radiomics models, and assess the impact of rib suppression on model performance. To establish these radiomics models, six machine learning models were utilized in the study. The performance was evaluated using the area under the receiver operating characteristic curve (AUC). On the validation set, the models demonstrated significant improvements after rib suppression. Specifically, the Gradient Boosting Machine (GBM) achieved an AUC of 0.781 post-suppression compared to 0.556 pre-suppression. Notably, Linear Discriminant Analysis (LDA) and Logistic Regression (LR) performed particularly well when combining features from both scenarios, achieving AUCs of 0.762 and 0.756. The results indicate the feasibility of developing radiomics models for diagnosing NRDS and highlight the enhancement in model performance due to rib suppression. This study provides a promising new method for the imaging diagnosis and prognosis evaluation of neonatal respiratory distress syndrome, showcasing the potential of radiomics in pediatric imaging.
      Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
      (© 2025. The Author(s).)
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    • Grant Information:
      2023CAMCHS003A17 Chinese Association for Maternal and Child Health Studies; ZR2024MH223 Natural Science Foundation of Shandong Province
    • Contributed Indexing:
      Keywords: Bone suppression; Chest X-ray; Deep learning; Neonatal respiratory distress syndrome; Radiomics
    • Publication Date:
      Date Created: 20250205 Date Completed: 20250205 Latest Revision: 20250208
    • Publication Date:
      20250208
    • Accession Number:
      PMC11799334
    • Accession Number:
      10.1038/s41598-025-88982-6
    • Accession Number:
      39910276