Tracheobronchial Tree Size as a Predictor of Disease Severity and Outcomes in Idiopathic Pulmonary Fibrosis

Ratwani, Ankush; King, Christopher R.; Brown, Whitney; Shlobin, Oksana A.; Weir, Nargues; Nathan, Steven D.

doi:10.1016/j.chest.2017.08.514

Cited by 1 publication

(3 citation statements)

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“…No correlation was seen between the volume of the trachea and %FVC predicted. This conclusion may be consistent with the findings of Ratwani et al [ 31 ], who found that there was no association between %FVC predicted and growing tracheobronchial tree size, indicating that tracheal expansion is not only due to fibrosis and that other variables may be at play. Such findings suggest that the increase of the volume of the trachea might be a good new handcrafted radiomic feature to serve as a promising tool in the diagnosis of IPF.…”

Section: Discussionsupporting

confidence: 92%

“…IPF is also associated with wide parenchymal and airway conditions, such as those found in the trachea wall, which leads to pathological changes [ 48 ]. Ratwani et al, studied the correlation between the change of tracheobronchial tree size and the disease severity of IPF [ 31 ]. Our study found a significant difference in the volume of trachea between normal, IPF/UIP and, ILDs patients.…”

Section: Discussionmentioning

confidence: 99%

“…The aims of this study are two-fold: (1) to evaluate the use of radiomics, to differentiate between normal lung tissue and ILDs; (2) to evaluate the use of radiomics to distinguish IPF with a typical or less typical (biopsy-proven) UIP pattern related to IPF from HRCT patterns related to non-IPF ILDs. We also conjecture, based on the literature [ 31 ], that tracheal enlargement and tracheal shape would significantly complement handcrafted radiomic features that would help in the classification of different types of ILDs.…”

Section: Introductionmentioning

confidence: 94%

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

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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.