Abstract
PurposeLung 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.MethodsA literature review was conducted using PubMed/Medline for search of relevant peer-reviewed publications from January 2012 to June 2020ResultsWe 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.ConclusionPromising 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.
Highlights
Death from lung cancer is estimated to be 1.7 millions each year worldwide, essentially due to late diagnoses [1], making it the first cause of cancer-related death in the world [2] despite recent discoveries in the field of tumor biology and new treatment strategies
The authors conducted a literature review using PubMed/ Medline in order 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
The gaps between stakeholders, vendors, researchers, clinicians, healthcare organization administrations, and purchasers, need to be reduced. Radiomics in this last decade shows good ability to be considered as a potential new biomarker at different steps of the patient’s care in lung cancer
Summary
Death from lung cancer is estimated to be 1.7 millions each year worldwide, essentially due to late diagnoses [1], making it the first cause of cancer-related death in the world [2] despite recent discoveries in the field of tumor biology and new treatment strategies. The emergence of new targeted treatment focusing on specific biomolecular alterations such as EGFR [3] and ALK mutations has led to a new paradigm of cancer care, so-called “personalized” medicine, to the historic “one-sizefits-all” medicine. Radiomics could play a role in patient-specific treatment adaptations. Radiomics is based on the innovative approach that computerized algorithms are able to process imaging exams into more complex quantitative data. They can be applied to different imaging modalities (ultrasound, CT, PET, conventional radiology) by analyzing in a selected region of interest (ROI) the distribution of signal intensities
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