A Handcrafted Radiomics-Based Model for the Diagnosis of Usual Interstitial Pneumonia in Patients with Idiopathic Pulmonary Fibrosis

Turkey Refaee, Benjamin Bondue, Gaetan Van Simaeys, Guangyao Wu, Chenggong Yan, Henry C Woodruff, Serge Goldman, Philippe Lambin*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The most common idiopathic interstitial lung disease (ILD) is idiopathic pulmonary fibrosis (IPF). It can be identified by the presence of usual interstitial pneumonia (UIP) via high-resolution computed tomography (HRCT) or with the use of a lung biopsy. We hypothesized that a CT-based approach using handcrafted radiomics might be able to identify IPF patients with a radiological or histological UIP pattern from those with an ILD or normal lungs. A total of 328 patients from one center and two databases participated in this study. Each participant had their lungs automatically contoured and sectorized. The best radiomic features were selected for the random forest classifier and performance was assessed using the area under the receiver operator characteristics curve (AUC). A significant difference in the volume of the trachea was seen between a normal state, IPF, and non-IPF ILD. Between normal and fibrotic lungs, the AUC of the classification model was 1.0 in validation. When classifying between IPF with a typical HRCT UIP pattern and non-IPF ILD the AUC was 0.96 in validation. When classifying between IPF with UIP (radiological or biopsy-proved) and non-IPF ILD, an AUC of 0.66 was achieved in the testing dataset. Classification between normal, IPF/UIP, and other ILDs using radiomics could help discriminate between different types of ILDs via HRCT, which are hardly recognizable with visual assessments. Radiomic features could become a valuable tool for computer-aided decision-making in imaging, and reduce the need for unnecessary biopsies.

Original languageEnglish
Article number373
Number of pages12
JournalJournal of Personalized Medicine
Volume12
Issue number3
DOIs
Publication statusPublished - Mar 2022

Keywords

  • AGREEMENT
  • CRITERIA
  • CT
  • DISEASE
  • FEATURES
  • LINE
  • QUANTIFICATION
  • RESOLUTION
  • SURGICAL LUNG-BIOPSY
  • YIELD
  • handcrafted radiomics
  • interstitial lung diseases
  • machine learning
  • usual interstitial pneumonia
  • Usual interstitial pneumonia
  • Interstitial lung diseases
  • Handcrafted radiomics
  • Machine learning

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