Computed tomography for the diagnosis of solitary thin-walled cavity lung cancer.

ABSTRACT

BACKGROUND AND AIM: Lung cancer is the most commonly diagnosed neoplasm and the leading cause of cancer-related death worldwide. Despite the high incidence of lung cancer, the diagnosis of solitary thin-walled cavity lung cancer is rare. The aim of this review is to explore the potentials of computed tomography (CT) as diagnostic tool for solitary thin-walled cavity lung cancer. METHOD: The literature search was made in electronic databases including PudMed, Ovid SP, Embase, Web of Sciences, EBSCO and Wiley online by using relevant key terms. Because of the rarity of the subject, no precise exclusion or inclusion criteria were used for article selection and the outcome dissemination was decided to be more descriptive rather than quantitative. RESULTS: The detection of cavitation in lungs is frequently done utilizing chest radiographs CT scans. However, the diagnostic challenge remains the accurate detection of solitary thin-walled cavity lung cancer among the prevalence of cavitary lung lesions in multiple thoracic disorders including benign disorders, infectious disease and malignant tumors. Moreover, an accurate diagnosis of solitary thin-walled cavity lung cancer is further complicated by its subjective classification within the literature. In order to facilitate early diagnosis of this disease and circumvent the need for more invasive tests that may not be warranted, the overarching goal is to establish definitive radiological features of lung cavities that are indicative of malignancy. Herein, we describe the benefits of using CT to identify and diagnose solitary thin-walled cavity lung cancer, as well as explore the underlying mechanisms that contribute to thin-walled cavity formation in oncology patients. CONCLUSION: CT is the best modality for the noninvasive differentiation between malignant and nonmalignant cavities as it provides reliable information regarding the morphology and density of lesions. Besides, CT densitometry can efficiently detect the calcifications in lesions.