Pulmonary & Respiratory Medicine

Differential Diagnosis of Non-Segmental Consolidations

Abstract

Attiya Haroon, Futoshi Higa, Shusaku Haranaga, Satomi Yara, Masao Tateyama, Haley L Cash, Takashi Ogura and Jiro Fujita

Consolidation and Ground-Glass Opacities (GGO) are common findings on chest Computed Tomographic (CT) scans. Consolidation and GGO can be divided into segmental, non-segmental and interstitial pneumonia types based upon distribution pattern. The aim of this review is to highlight the importance of the non-segmental distribution pattern, and to explain its relevancy in various conditions. Non-segmental distribution pattern presents as lobar pneumonia histologically, whereas segmental distribution appears as bronchopneumonia. The different diagnoses that can be derived from non-segmental distribution consist of infectious pneumonias caused by S. pneumoniae, K. pneumoniae and L. pneumophila, Chlamydophila psittaci, M. pneumoniae, measles, viral pneumonia and tuberculosis, as well as non-infectious inflammatory diseases including Cryptogenic Organizing Pneumonia (COP), Chronic Eosinophilic Pneumonia (CEP) and Pulmonary Alveolar Proteinosis (PAP), and other conditions including Anti-Neutrophil Cytoplasmic Antibody (ANCA) related pulmonary hemorrhage and Bronchoalveolar Carcinoma (BAC). A detailed analysis and correlation of non-segmental distribution with other CT findings (e.g., ground-glass opacity, interlobular septal thickening, fibrosis, pleural effusion, air trapping, nodular lesions, and air bronchiologram and air bronchogram) can facilitate the accurate diagnosis of these respiratory diseases. Specifically of interest is L. pneumophila infection, which first presents as bronchopneumonia but later converts into lobar pneumonia. The mechanism behind this conversion involves inflammatory exudates that can pass through Kohn’s pores and Lambert’s channel during L. pneumophila infection. Therefore, L. pneumophila pneumonia pattern first presents as segmental at the start of infection and after 2 weeks or more converts into non-segmental type. Overall, this review demonstrates that the nonsegmental distribution pattern can be very valuable in making respiratory disease diagnosis.