Candidate Identification Technique for Lung Cancer

Candidate Identification Technique for Lung Cancer

Sadaf Batool Naqvi (Johns Hopkins Aramco, Saudi Arabia) and Abad Ali Shah (UET, USA)
Copyright: © 2021 |Pages: 13
DOI: 10.4018/IJRQEH.2021010101
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Intensive research work has been done related to lung cancer prognosis. However, the current research mainly emphasises on decreasing the mortality rate, and increasing the survival rate of lung cancer patients. In this paper, the authors argue that an early identification and candidate identification (CI) of this disease can change the early detection treatment of lung cancer and hence can markedly reduce the mortality rate. The proposed technique CI will recognize the disease well in advance and can potentially save the candidate's life. In other words, a candidate of lung cancer is identified and treated in Stage 0 (explained later) instead of in Stage 1 or in the later stages of the lung cancer. In this paper, the authors have introduced a technique, called candidate identification, to identify candidates of the lung cancer. In the proposed technique, a backward forecasting function (BFF) is also proposed to generate Stage 0 data of the patients who have already lung cancer.
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Bioinformatics is an emerging area, and recently there have been evolutionary developments in this area (Albayraktaroglu, Jaleel, Xue et al., 2005). The primary purpose of this area is to manage biological and medical data (Bayat, 2002; Henderson, 2009; “Lung Cancer”, 2011). The recent studies and reviews show the importance of bioinformatics in the future (Kanehisa & Bork, 2003; “Lung Cancer”, 2011). This area mainly emphasizes on the modeling of large scale and complex biological systems into complete computer-based systems (Henderson, 2009; Kanehisa & Bork, 2003). In other words, bioinformatics is defined as a database management system (DBMS) that provides facility to the users to store, retrieve and manipulate biological data. Bioinformatics plays a vital role in the field of biological research, medicine and drug discovery (Bayat, 2002; Henderson, 2009). Furthermore, it is also essential in cancer-related studies.

Cancer is a disease in which the cells of a human body rapidly increase abnormally (Jemal, Siegel, Xu et al., 2010). The genetic material of a cell is responsible for changing its behavior. The damaged part of Deoxyribonucleic Acid (DNA) which codes the characteristics of a cell in the cell replication process can produce tumors. These tumors are either malignant or benign (Jemal, Siegel, Xu, & Ward, 2010). Recent studies have shown that lung cancer is the most common type of cancer (Jemal, Tiwari, Murray et al., 2004). The cancerous cells proliferate in the lungs and metastasize to other organs through blood vessels, as shown in Figure 1.

Lung cancer is further classified into two categories; Non-small cell lung cancer (NSCLC) and Small cell lung cancer (SCLC) (Jemal, Bray, Center et al., 2011). NSCLC is the most common type of lung cancer. It usually grows and spreads slowly than SCLC. NSCLC has four (4) stages starting from Stage 1 to Stage 4 (see Figure 4). The life expectancy of a lung cancer patient decreases when it reaches its advanced stages.

Lung cancer is death-causing cancer. Patients who have lung cancer have reduced life expectancy than normal. It is diagnosed, mostly in its advanced stages because its symptoms, usually, do not appear in the initial stages. The late diagnosis of this cancer leads to a low survival rate. According to a survey in 2004, 92% of patients diagnosed with lung cancer in the USA died (Hakulinen & Pukkala, 1981). This mortality rate of lung cancer worldwide has proved to be 86% as reported in (Didkowska, Wojciechowska, Koskinen et al., 2011). Early diagnosing/forecasting techniques for lung cancer involves the studies related to habits of patients and risk factors of cancer (Cesare & Murphy, 2011; Hasegawa, Sone, Takashima et al., 2000; Honda, Johkoh, Sekiguchi et al., 2009). Current research on lung cancer mainly focuses on decreasing the mortality rate of lung cancer patients by analyzing different methods of CT scan images and chest radiographs(Anand, Kunnumakkara, Sundaram et al., 2008; Garcia, Jemal, Ward et al., 2007; Reich & Kim, 2011).

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