Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. (July 2021)

• Record Type: Journal Article
• Authors: Cucchiara, Federico; Petrini, Iacopo; Romei, Chiara; Crucitta, Stefania; Lucchesi, Maurizio; Valleggi, Simona; Scavone, Cristina; Capuano, Annalisa; De Liperi, Annalisa; Chella, Antonio; Danesi, Romano; Del Re, Marzia
• Is Part Of: Pharmacological research. Volume 169(2021)
• Publication Date: 2021-07
• Subjects: Lung cancer -- Liquid biopsy (LB) -- Radiomics -- Artificial Intelligence (AI) -- Precision medicine; Pharmacology -- Periodicals; Pharmacology -- Periodicals; Research -- Periodicals; Médicaments -- Recherche -- Périodiques; Pharmacologie -- Périodiques; 615.105
• DOI: 10.1016/j.phrs.2021.105643
• Languages: English
• ISSNs: 1043-6618

Abstract

Abstract: Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a 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 suggest 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, improvement of patients' quality of life, 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 directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles 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 a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer. Graphical Abstract: ga1 Highlights: Liquid biopsy-derived tumor components may have diagnostic, prognostic and predictive utility in lung cancer. Growing evidence suggest radiomic analysis to decode the tumor pathophysiological processes and support patient management an. Risk stratification and monitoring are the most promising applications for both liquid biopsy and radiomics in lung cancer. Artificial intelligence may implement multiparametric information from radiomics and liquid biopsy into clinical practice.