Purpose: Lung cancer represents the first cause of cancer-related death in the world. Radiomics studies arise rapidly in this late decade. The aim of this review is to identify important recent publications to be synthesized into a comprehensive review of the current status of radiomics in lung cancer at each step of the patients' care.
Methods: A literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020.
Results: We identified several studies at each point of patient's care: detection and classification of lung nodules (n=16), determination of histology and genomic (n=10) and finally treatment outcomes predictions (=23). We reported the methodology of those studies and their results and discuss the limitations and the progress to be made for clinical routine applications.
Conclusion: Promising perspectives arise from machine learning applications and radiomics based models in lung cancers, yet further data are necessary for their implementation in daily care. Multicentric collaboration and attention to quality and reproductivity of radiomics studies should be further consider.
Keywords: lung cancer; lung cancer screening; machine learning; oncology; radiomics; treatment outcome and efficiency.
Copyright © 2021 El Ayachy, Giraud, Giraud, Durdux, Giraud, Burgun and Bibault.