Clinical and radiographic differentiation of lung nodules caused by mycobacteria and lung cancer: a case–control study

Abstract

BackgroundLung nodules caused by mycobacteria can resemble lung cancer on chest imaging. The advent of lung cancer screening with low-dose Computed Tomography is accompanied by high false-positive rates, making it necessary to establish criteria to differentiate malignant from benign nodules.MethodsWe conducted a retrospective case–control study of 52 patients with mycobacterial lung nodules and 139 patients with lung cancer, diagnosed between 2010 and 2012. We compared clinical and radiographic characteristics to identify predictors of disease by univariate and multivariate analysis. The discriminatory power of maximum Standardized Uptake Values from Positron-Emission-Tomography was also evaluated.ResultsSeveral variables were correlated with a diagnosis of mycobacterial infection or lung cancer on univariate analysis. Such variable include smoking status and history, lesion size and imaging evidence of tree-in-bud opacities, lymphadenopathy or emphysema on computed tomography. Upon author consensus, the most clinically-relevant variables were selected to undergo multivariate analysis. A history of current or former smoking [OR 4.4 (95 % CI 1.2–15.6) and 2.7 (95 % CI 1.1–6.8), respectively P = 0.04] was correlated with diagnoses of lung cancer. Contrarily, the presence of tree-in-bud opacities was less likely to be correlated with a diagnosis of malignancy [OR 0.04 (95 % CI 0.0–1.0), P = 0.05]. Additionally, higher maximum standardized uptake values from positron emission tomography were associated with malignancy on multivariate analysis [OR 1.1 (95 % CI 1.0–1.2), P = 0.04]; but the accuracy of the values in differentiating between diseases was only 0.67 as measured by the area under the curve. Lesion size was not independently associated with diagnosis [OR 0.5 (95 % CI 0.2–1.2), (P = 0.12)].ConclusionsEstablishing the likelihood of malignancy for lung nodules based on isolated clinical or radiographic criteria is difficult. Using the variables found in this study may allow clinicians to stratify patients into groups of high and low risk for malignancy, and therefore establish efficient diagnostic strategies.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-015-1185-4) contains supplementary material, which is available to authorized users.

Diameter, Thickness and Discordance Degree of Solitary Pulmonary Cavitary Lesion to Differentiate Benign From Malignant Lesions, a Mutlislice Ct Study.

“…The present study showed that the means diameter and thickness of malignant cavitary lesion were significantly higher than benign cavitary lesions (p<0.001). This finding coincides with the results of Figueroa et al 15 18 . Malignant pulmonary cavitation characterized by irregular or round appearance with discrepancy in wall thickness.…”

Benign features of infection‐related tumor‐like lesions of the lung: A retrospective imaging review study

“…Using computed tomography (CT) to screen for lung cancer is highly recommended but frequently causes a high false‐positive rate of around 70% . Considering the high frequency of benign lesions, differentiation between benign and malignant lesions before invasive biopsy is becoming increasingly important …”

“…[3][4][5][6][7][8][9] Considering the high frequency of benign lesions, differentiation between benign and malignant lesions before invasive biopsy is becoming increasingly important. 10,11 Many radiological features of pulmonary infections have been demonstrated in CT images, such as lobar, segmental, or alveolar consolidation with blurred margins, ground glass opacities, centrilobular nodules, the tree-in-bud sign, reticulation, cavitation, wedgeshaped consolidation, bronchiectasis, necrotic lymphadenopathy, smooth inner wall of cavitation, calcification, among others. [12][13][14] However, malignancies can share similar radiological patterns.…”

Mycobacterium szulgai Lung Disease or Breast Cancer Relapse—Case Report

“…However, studies on the differentiation between benign and malignant lesions using 18 F-FDG PET/CT have been inconclusive. Figueroa et al showed that the specificity of 18 F-FDG PET/CT is not sufficient to differentiate between lung nodules caused by NTM and cancer, as an increased maximum standardized uptake value (SUV max) is obtained in both types of lesion [16]. According to the authors, an SUV max value ≥ 16 may be more specific for cancer diagnosis as such values were not observed in NTM lesions.…”