"Lung cancer has posed a major challenge for health institutions all around the world. Economic and social implications derived from this disease result in efforts to reduce mortality rates and establish better diagnostic procedures. Specifically, technologies such as low-dose computerized tomography (CT) have been implemented to increase early detection accuracy of lung cancer. Even though, there are still major challenges to improve sensitivity and specificity rates of lung cancer prognosis using CT. More precisely, false positives and negatives are still present in the prognosis procedure. In consequence, there are psychological, economic and social problems associated with false positive and negative rates. Some of these problems include economic costs for families and health institutions, patient anxiety, and potential risks of morbidity and/or mortality. Moreover, false negatives represent the main problem due to the potential irreversible consequences that could arise, where survival rates decrease considerably and paliative care is the only alternative to reduce patient suffering. To address these issues, several research studies have developed different Computer Aided Diagnostic (CAD) tools. Thus, the objective of this study is to investigate all related work to develop a better CAD system for automatic lung cancer detection that will help radiologists with CT assessment; and ultimately, will reduce the number of false positives and negatives."