Differential diagnosis of localized pneumonic-type lung adenocarcinoma and pulmonary inflammatory lesion

Abstract

Background In clinical practice, a number of delayed diagnoses of localized pneumonic-type lung adenocarcinoma (L-PLADC) mimicking pneumonia have been identified due to the lack of knowledge regarding the radiological findings associated with this condition. Here, we defined L-PLADC as a special type of lung adenocarcinoma that presents as a focal consolidation involving < 50% of the area of a lobe and aimed to investigate the differential clinical and imaging features between L-PLADC and localized pulmonary inflammatory lesion (L-PIL). Results The data of 120 patients with L-PLADC and 125 patients with L-PIL who underwent contrast-enhanced chest computed tomography (CT) scan were retrospectively analyzed. For clinical characteristics, older age, women, nonsmokers, and no symptom were more common in L-PLADC (all p < 0.001). With regard to CT features, air bronchogram, irregular air bronchogram, ground-glass opacity (GGO) component, and pleural retraction were more frequently observed in L-PLADC, while necrosis, satellite lesions, halo sign, bronchial wall thickening, interlobular septa thickening, pleural attachment, and pleural thickening were more commonly seen in L-PIL (all p < 0.001). Multivariate analysis showed age ≥ 58 years, female sex, GGO component, irregular air bronchogram, pleural retraction, and the absence of necrosis and pleural attachment were the most effective variations associated with L-PLADC with an AUC of 0.979. Furthermore, an external validation cohort containing 62 patients obtained an AUC of 0.929. Conclusions L-PLADC and L-PIL have different clinical and imaging characteristics. An adequate understanding of these differential features can contribute to the early diagnosis of L-PLADC and the subsequent therapeutic strategy.

Differentiation of localized pneumonic-type lung adenocarcinoma from localized pulmonary inflammatory lesion based on clinical data and multi-slice spiral computed tomography imaging features

“…In addition, the findings of this study suggested that calcification, satellite lesions, and thickening of the adjacent bronchial wall and interlobular septum were more common in inflammatory lesions, which may be related to the diffusion of inflammation along the adjacent alveoli, bronchi, and mesenchyme. In contrast to the literature ( 3 ), the proportion of calcification and satellite lesions in this group was slightly higher, as was the proportion of pulmonary tuberculosis cases, which may be related to the relatively high incidence of calcification and surrounding satellite lesions in tuberculosis. Similarly, pleural thickening and pleural effusion were also highly indicative of inflammatory lesions.…”

“…Our results also showed that necrosis is more common in inflammatory lesions, which is consistent with the findings of previous studies ( 3 , 8 ). Inflammatory necrosis is related to the inflammatory response caused by microbial infection.…”

“…Clinically, primary lung adenocarcinoma with patchy, large patchy consolidation, or ground-glass shadow on chest computed tomography (CT) images is usually called pneumonic-type lung adenocarcinoma (PLADC) (1,2). Previous studies (3)(4)(5)(6)(7)(8) have divided PLADC into localized pneumonic-type lung adenocarcinoma (L-PLADC) and diffuse lung adenocarcinoma. L-PLADC is defined as a PLADC with a single lobe and isolated localized lesions that make up less than 50% of the lobe area.…”

Artificial intelligence driven digital whole slide image for intelligent recognition of different development stages of tongue tumor via a new deep learning framework

“…7,8 The diagnosis was made by analyzing the morphological variation in the cancerous area, quantifying the density of the malignant area, and observing the spatial arrangement of the tumor microenvironment. [9][10][11] Nevertheless, the scarcity of pathologists and high clinical experience requirements exacerbate the conflict between clinical demands and actual production. In addition, intra-and inter-observer differences introduce additional bias and risk to histopathological analysis in the diagnostic processes.…”

“…In the course of routine diagnosis, digital whole slide image (DWSI) of hematoxylin and eosin (H&E) stained tumor tissue slice is manually magnified by a trained histopathologist through a computer 7,8 . The diagnosis was made by analyzing the morphological variation in the cancerous area, quantifying the density of the malignant area, and observing the spatial arrangement of the tumor microenvironment 9–11 . Nevertheless, the scarcity of pathologists and high clinical experience requirements exacerbate the conflict between clinical demands and actual production.…”