| Literature DB >> 27495787 |
Abstract
Pulmonary nodules are the most frequent pathological finding in low-dose computed tomography (CT) scanning for early detection of lung cancer. Early stages of lung cancer are often manifested as pulmonary nodules; however, the very commonly occurring small nodules are predominantly benign. These benign nodules are responsible for the high percentage of false positive test results in screening studies. Appropriate diagnostic algorithms are necessary to reduce false positive screening results and to improve the specificity of lung cancer screening. Such algorithms are based on some of the basic principles comprehensively described in this article. Firstly, the diameter of nodules allows a differentiation between large (>8 mm) probably malignant and small (<8 mm) probably benign nodules. Secondly, some morphological features of pulmonary nodules in CT can prove their benign nature. Thirdly, growth of small nodules is the best non-invasive predictor of malignancy and is utilized as a trigger for further diagnostic work-up. Non-invasive testing using positron emission tomography (PET) and contrast enhancement as well as invasive diagnostic tests (e.g. various procedures for cytological and histological diagnostics) are briefly described in this article. Different nodule morphology using CT (e.g. solid and semisolid nodules) is associated with different biological behavior and different algorithms for follow-up are required. Currently, no obligatory algorithm is available in German-speaking countries for the management of pulmonary nodules, which reflects the current state of knowledge. The main features of some international and American recommendations are briefly presented in this article from which conclusions for the daily clinical use are derived.Entities:
Keywords: Diagnostic algorithms; Early detection of cancer; Image processing; Low dose CT; Lung neoplasms
Mesh:
Year: 2016 PMID: 27495787 DOI: 10.1007/s00117-016-0150-6
Source DB: PubMed Journal: Radiologe ISSN: 0033-832X Impact factor: 0.635