RadPleura: a Radiomics-based Framework for Lung Pleura Classification in Histology Images from Interstitial Lung Diseases

The diagnostic approach of interstitial lung diseases (ILDs) may be a challenge and requires a multidisciplinary discussion (MDD). While the definition of histological patterns of surgical lung biopsy by pathologists is helpful to the final diagnosis of MDD, its recognition could be challenging due to various degrees of lung remodeling and distortion. The pleura is a reference structure to identify some histological findings for the recognition of a pathological ILD pattern. When a pattern is found, it is important to know whether it is close to the pleura to determine its specific type and severity. This manual process is a tedious and laborious one for the pathologist. Automating this task is important for a complete computed-system-assisted ILD diagnostic process. We introduce ``RadPleura'' a framework for pleura classification of histopathological images using a radiomics-based approach. Our framework performs image pre-processing, region-of-interest segmentation, and extraction of a radiomic-signature fitted to ILD classification. To evaluate the radiomic-signature, we classified it into pleura and non-pleura, using two classifiers: Support Vector Machine and Gradient-boosted Decision Trees. Our experiments yield promising results, with F-scores of 92% for SVM, and 91% for GBD. We also created a dataset of lung histopathology images with respective ground truth for pleura classification. To the best of our knowledge, this study is the first disclosed attempt to explore and develop a radiomic-signature for pleura classification. The methods and techniques are integrated into the RadPleura framework developed.