Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Abstract

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology”

“…The findings showed that the DL model outperformed visual CT analysis in predicting the histopathologic diagnosis of UIP and exhibited a higher reproducibility compared to expert radiologists. Specifically, when classifying cases as probable UIP based on a guideline, the DL model achieved a higher specificity compared to expert radiologists [43][44][45]. These models can learn to identify subtle radiological features indicative of specific ILD subtypes, including honeycombing, ground-glass opacities, reticulation, and traction bronchiectasis [12,13,46].…”

Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study

“…As the need for computer-aided CT analysis for ILD has increased, various methods of quantitative CT analysis have emerged, such as densitometry, texture analysis, and artificial intelligence (AI)-based methods [ 10 ]. AI-based image analysis has advantages in terms of high reliability and reproducibility over other methods.…”

Steady Progress in Interstitial Lung Disease

“…Classification systems for ILDs and their mimics are being refined, 3,4 and imaging remains one of the fundamental tools for diagnosis and characterization, as highlighted by Sneider and Kershaw 5 . While there have not been any recent paradigm-shifting hardware changes in the realm of high-resolution computed tomography, there are ongoing exciting developments in quantitative imaging and machine learning/artificial intelligence, as described by Suman and Koo 6 . As important as imaging is, the most reliable diagnosis requires a multidisciplinary team, and Stowell et al 7 illustrate how radiology fits into that team through a case-based review.…”

“…5 While there have not been any recent paradigm-shifting hardware changes in the realm of high-resolution computed tomography, there are ongoing exciting developments in quantitative imaging and machine learning/artificial intelligence, as described by Suman and Koo. 6 As important as imaging is, the most reliable diagnosis requires a multidisciplinary team, and Stowell et al 7 illustrate how radiology fits into that team through a case-based review.Although the medical management of fibrotic ILDs has traditionally been limited to those patients specifically with idiopathic pulmonary fibrosis, the FDA approved the expansion of nintedanib for the broader category of progressive fibrosing ILD in 2020, after it showed efficacy in this patient population. 8 Although this might ultimately allow a simplification of our current imaging classification system, differentiating the appropriate disease pattern is still currently required for both diagnostic and prognostic purposes, and Oscarelli et al 9 and Adams et al 10 provide reviews of autoimmune ILDs and idiopathic ILDs, respectively.…”