Histologic Analysis of Idiopathic Pulmonary Fibrosis by Morphometric and Fractal Analysis

Abstract

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disorder, ultimately leading to respiratory failure and death. Despite great research advances in understanding the mechanisms underlying the disease, its diagnosis, and its treatment, IPF still remains idiopathic without known biological or histological markers able to predict disease progression or response to treatment. The histologic hallmark of IPF is usual interstitial pneumonia (UIP), with its intricate architectural distortion and temporal inhomogeneity. We hypothesize that normal lung alveolar architecture can be compared to fractals, such as the Pythagoras tree with its fractal dimension (Df), and every pathological insult, distorting the normal lung structure, could result in Df variations. In this study, we aimed to assess the UIP histologic fractal dimension in relationship to other morphometric parameters in newly diagnosed IPF patients and its possible role in the prognostic stratification of the disease. Clinical data and lung tissue specimens were obtained from twelve patients with IPF, twelve patients with non-specific interstitial pneumonia (NSIP), and age-matched “healthy” control lung tissue from patients undergoing lung surgery for other causes. Histology and histomorphometry were performed to evaluate Df and lacunarity measures, using the box counting method on the FracLac ImageJ plugin. The results showed that Df was significantly higher in IPF patients compared to controls and fibrotic NSIP patients, indicating greater architectural distortion in IPF. Additionally, high Df values were associated with higher fibroblastic foci density and worse prognostic outcomes in IPF, suggesting that Df may serve as a potential novel prognostic marker for IPF. The scalability of Df measurements was demonstrated through repeated measurements on smaller portions from the same surgical biopsies, which were selected to mimic a cryobiopsy. Our study provides further evidence to support the use of fractal morphometry as a tool for quantifying and determining lung tissue remodeling in IPF, and we demonstrated a significant correlation between histological and radiological Df in UIP pattern, as well as a significant association between Df and FF density. Furthermore, our study demonstrates the scalability and self-similarity of Df measurements across different biopsy types, including surgical and smaller specimens.

Complex shape markers can detect alterations in the spatial distribution of cell nuclei in human lung squamous cell carcinoma: a useful tool for automatic analysis?

“…It is worth mentioning that the fractal dimension has also been used recently to detect alterations in other non-cancer lung pathologies. Mancini et al (2023) provided evidence to support the use of fractal morphometry as a tool for quantifying and determining lung tissue remodeling in idiopathic pulmonary brosis. It is also worth highlighting the increase in the fractal dimension of cell nuclei in carcinoma of the oral mucosa (Mincione et al 2015).…”

“…Many anatomical structures have been shown to demonstrate self-similarity properties, such as the lung with its bronchioalveolar rami cations. In this sense, fractal morphometry assessing architectural distortion and correlating with a speci c pattern can enhance the recognition of histological structures (Mancini et al 2023).…”

Oxidopamine-Induced Nuclear Alterations Quantified Using Advanced Fractal Analysis: Random Forest Machine Learning Approach

“…In contrast to the fractal dimension, lacunarity refers to the distribution and size of gaps in the analyzed structural pattern over different scales. Homogeneous systems with uniform or clustered distribution of structural voids tend to have relatively low values of this parameter [16,18].…”

“…Application of fractal measures in random forest model development can be computationally intensive especially since it often requires a large number of calculations on high-quality micrographs or regions of interest. Results on fractal indicators also often significantly vary across different computational platforms, and different programs for calculation of fractal dimension, despite using the same methodology, often yield different outputs [14,[16][17][18]. Furthermore, from our own experience and based on literature data, indicators such as fractal dimension and lacunarity greatly depend on the type of staining, the protocol used during image acquisition (i.e., white balance, contrast, hue, etc.…”

“…The details on the ImageJ software and its functions can be found in the previously published work [15]. This platform can efficiently compute a number of fractal indicators for a specified structural pattern in a digital image and has been previously used on numerous occasions in biomedical research [16][17][18]. We applied the box-counting method, in which a grid of boxes (two-dimensional squares) is superimposed over a pattern after which the program counts the number of boxes (N) that contain a part of the pattern.…”