BACKGROUND
Lung cancer is considered to be the most fatal out of all diagnoseable cancers. This is, in part, due to the difficulty in detecting lung cancer at an early stage. Moreover, approximately one in five individuals who will develop lung cancer will pass away due to a misdiagnosis. Fortunately, Machine Learning (ML) and Deep Learning (DL) is considered to be a promising solution for detection of lung cancer through developments in radiology.
OBJECTIVE
The purpose of this paper is to is to review how AI can assist identifying and diagnosing of lung cancer in an early stage.
METHODS
PRISMA was utilized and were retrieved from 4 databases: Google Scholar, PubMed, EMBASE, and Institute of Electrical and Electronics Engineers (IEEE). In addition, two phases of screening were implemented in order to determine relevant literature. The first phase was reading the title and abstract, and the second stage was reading the full text. These two steps were independently conducted by three reviewers. Finally, the three authors use a narrative synthesis to present the data.
RESULTS
Overall, 543 potential studies were extracted from four databases. After screening, 26 articles that met the inclusion criteria were included in this scoping review. Several articles utilized privet data including patients’ data and other public sources. 15 articles used data from UCI repository dataset (58%). However, CT scan images was utilized on 9 studies (normal CT was mentioned in 5 articles (19%), two studies used CT scan with PET (7.7%), and two articles used FDG with CT (7.7%). While two articles used demographic data such as age, sex, and educational background (7.7%).
CONCLUSIONS
This scoping review illustrates recent studies that utilize AI models to diagnose lung cancer. The literature currently relies on private and public databases and compare models with physicians or other machine learning technology. Additional studies should be conducted to explore the efficacy of these technologies in clinical settings.