The largest cause of cancer-related fatalities worldwide is lung cancer. It displays phenotypic traits at the mesoscopic scale that are typically invisible to the human eye but can be non-invasively recorded as radiomics features on medical imaging, which can create a high-dimensional data space that is accessible to machine learning. Precision medicine is made possible by the use of radiomics characteristics, which may be harnessed and applied in an artificial intelligence paradigm to risk stratify patients, predict histology and molecular results, and clinical outcome measurements. Radiomics-based procedures are preferable than tissue sampling-based strategies because they are non-invasive, repeatable, less expensive, and less prone to intra-tumoral heterogeneity. This study focuses on the combination of radiomics and artificial intelligence for providing precision medicine in the treatment of lung cancer, with the debate focused on innovative and ground-breaking works as well as potential future research areas. The largest cause of cancer-related fatalities worldwide is lung cancer. Globally, 2.2 million new cases and 1.8 million fatalities from cancer were reported in 2020, accounting for 21% of all cancer-related deaths. Lung cancer is a serious health concern due to its high prevalence and fatality. Lung cancer can develop at different locations in the bronchial architecture, which can result in a range of initial clinical presentations, from asymptomatic to include haemoptysis and cachexia. Over 70% of patients only receive a diagnosis for lung cancer when it has already progressed to an advanced stage since the disease's symptoms, if any, are non-specific. Its low mean 5-year survival rate of 20–30% is a result of this. Lung cancer is a diverse illness. Entity with a variety of development patterns and histological subtypes. The majority of instances of lung cancer are non-small cell lung cancer, which is the most common histological form. Squamous cell cancer is one of these.
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