Pneumoconiosis, an occupational respiratory illness triggered by inhaling mineral dust with increasing prevalence and severity worldwide. Chest radiograph plays a vital role in the screening of pneumoconiosis. The pneumoconiosis staging mainly depends on the small opacity in the lung fields, and early-stage pneumoconiosis staging has been a challenging task, necessitating quantitative diagnostics. Thoracic computed tomography (CT) images represent the gold-standard modality for evaluating local abnormalities and understanding structure-function relationships in an organ. The thoracic CT images have the potential to provide an essential feature for distinguishing subtle morphological patterns of micronodule distributions in the lungs through quantitative analyses. This study investigates whether quantitative representations based on topological data analysis can capture particulate shadows' three-dimensional (3D) distribution properties in pneumoconiosis in volumetric CT images.