Exploring the potential of Radiomics in identification and treatment of lung cancer: A systematic evaluation

Lung cancer is one of the most serious and life-threatening diseases in the world. Imaging modalities like computed tomography (CT) and Positron emission tomography (PET) play a crucial role in cancer diagnosis. Radiomics is an emerging field in medical imaging that uses advanced computational algorithms to extract quantitative features from medical images. Machine learning makes radiomics method of cancer diagnosis easier and more efficient by automating the process of feature selection and classification, which can save time and reduce the risk of human error in the diagnosis. It has the potential to revolutionize cancer detection by providing clinicians with valuable insights into tumour biology that can help in clinical decision-making and improve patient care outcomes. In this review paper, we primarily summarize the workflow of radiomics studies in the context of lung cancer and discussed the practical uses of radiomics in lung cancer, such as malignant tumour identification, classification of histologic subtypes, identification of tumour genotypes, and prediction of treatment response. Additionally, the paper addresses the key challenges associated with the clinical transition of radiomics, the limitations of current approaches, and potential future directions in this field.