Lung cancer has become a paradigm for the future of precision medicine in oncology, and liquid biopsy (LB) and radiomics may have great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggests that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, patient's quality of life improvement, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy research findings directly into clinical practice, an artificial intelligence (AI)-based system could help to coherently link patient clinical data together with respective of tumor molecular profile and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing the complementary and synergistic combination of LB and imaging to provide an attractive choice to the traditional molecular profile in the personalized treatment of lung cancer.
Keywords: Lung cancer; artificial intelligence (AI); liquid biopsy (LB); precision medicine; radiomics.
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