Lung Cancer Is Still One Of The Leading Causes Of Cancer-related Deaths

The number of lung cancer-related deaths has been drastically lowered over the past ten years mainly due to the decline in number of smokers, advances in early detection, and improved treatment options; however, lung cancer is still one of the leading causes of cancer-related deaths among both men and women (Siegel, Miller, & Jemal, 2019). It has been reported that in 2017 more people had died from this disease alone than from breast, prostate, colorectal, and brain cancers together (Siegel, Miller, & Jemal, 2020).

It was projected that 228,150 new lung cancer cases (13% males and 13% females affected) and 142,670 lung cancer-related deaths (24% males and 23% females affected) will occur in the U.S. in 2019, while these numbers predicted for 2020 are 228,820 (13% males and 12% females affected) and 135,720 (23% males and 22% females affected), respectively (Siegel et al.

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, 2019; Siegel et al., 2020). Based on these predictions, it can be observed that the number of new cases affecting females will be lower than the number of new cases affecting males, and that there will be fewer lung cancer-related deaths affecting both sexes (Siegel et al., 2019; Siegel et al., 2020).

5-year survival rates for the lung cancer are still pretty low in comparison to some other types of cancer and is only 5% (Siegel et al., 2020). This is mainly due to the most patients being diagnosed after the cancer had already spread to other body parts. If the lung cancer is detected in early phase and patients are treated promptly with the customized treatments, the 5-year survival rate is estimated to be 57%, which is significantly higher (Siegel et al., 2020). In order to detect lung cancer in early phase, characterize the different types, identify certain lung cancer associated gene mutations at a faster pace, and choose an appropriate clinical trial or customize a treatment plan for pulmonary oncology patients, Artificial Intelligence (AI) should be used (Bi et al., 2019; Coccia, 2020; Rabbani, Kanevsky, Kafi, Chandelier, & Giles, 2018; 2018; Xu et al., 2019).

The literature that discusses the importance of using AI in personalized medicine, particularly in pulmonary oncology was reviewed. Peer-reviewed databases such as PubMed, EMBASE, CINAHL, Google Scholar, COCHRANE library, and other online sources were searched through UIC library for relevant articles. The following key words were used in article search: “cancer statistics,” “lung cancer,” “pulmonary oncology,” “personalized medicine,” “non-small cell lung cancer (NSCLC),” and “artificial intelligence (AI).” Search was limited to English-language articles published from 2010 to 2020, and peer-reviewed articles that did not fit search criteria and duplicates were excluded.

Search of PubMed provided 2362 hits, however, only 15 articles were deemed relevant. On the other hand, search of CINAHL provided fewer hits – only 14, with only 2 being relevant. EMBASE search provided 538 hits, with 6 articles being relevant and 5 being duplicated previously found through PubMed. Lastly, search of Google Scholar provided the highest number of articles, 35,700, however, only 13 were relevant and out of those 13, 7 were excluded duplicates. Other databases that were search included COCHRANE library and IEEE Explore, however, these databases did not provide any hits.