Lung cancer (LC) is one of the most common malignant cancers worldwide representing the leading cause of death. Tobacco smoking is the main aetiological factor in lung carcinogenesis, but long-term exposure to air pollution and genetic susceptibility may play a role. In the last few decades , the diagnosis and therapies of LC have shown great improvement, but its clinical management is still challenging due to diagnosis mainly at advanced stages. Thus, there is an ever-increasing need to develop innovative, easy-to-use, and non-invasive diagnostic methodologies to implement within large-scale screening protocols to enable early detection and improve the management and clinical outcome . Recently, the volatile organic compounds (VOCs) analysis in exhaled breath, known as breathomics, has showed to be promising to identify disease-specific metabolic signatures. In this study, we analysed breath samples to detect and characterize by deep learning the breath fingerprint of patients with lung cancer.
P1.04.47 Breathomics Meets Deep Learning: A Novel Approach for Early Detection of Lung Cancer
Di Gilio, A.;Palmisani, J.;Nisi, M.;Pizzillo, V.;Pisconti, S.;Bellantuono, L.;Bellotti, R.;Diacono, D.;Tommasi, R.;Lo Sasso, A.;Varesano, N.;Montagna, E. S.;Pesola, F.;Marech, I.;
2025-01-01
Abstract
Lung cancer (LC) is one of the most common malignant cancers worldwide representing the leading cause of death. Tobacco smoking is the main aetiological factor in lung carcinogenesis, but long-term exposure to air pollution and genetic susceptibility may play a role. In the last few decades , the diagnosis and therapies of LC have shown great improvement, but its clinical management is still challenging due to diagnosis mainly at advanced stages. Thus, there is an ever-increasing need to develop innovative, easy-to-use, and non-invasive diagnostic methodologies to implement within large-scale screening protocols to enable early detection and improve the management and clinical outcome . Recently, the volatile organic compounds (VOCs) analysis in exhaled breath, known as breathomics, has showed to be promising to identify disease-specific metabolic signatures. In this study, we analysed breath samples to detect and characterize by deep learning the breath fingerprint of patients with lung cancer.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
