LIQUID BIOPSY AI-BASED ANALYSIS OF CIRCULATING TUMOR DNA FOR EARLY DIAGNOSIS AND RELAPSE PREDICTION IN LUNG CANCER

Abstract

One of the most important reasons lung cancers is one of the leading causes of cancer-related deaths is that many patients are diagnosed at a late stage or recur after treatment. Circulating tumor DNA (ctDNA) is a liquid biopsy that allows the detection of genetic changes originated in the tumor cells by analyzing a blood sample, which could help for earlier diagnosis, monitoring treatment and predicting relapse. This paper examines a liquid artificial intelligence (AI) platform for ctDNA analysis in lung cancer that leverages the combination of genomic mutation profiling, frag entomic features, methylation signatures and clinical data to enhance diagnostic and prognostic capacities. The proposed method uses machine learning and deep learning models to detect ctDNA patterns related to early lung cancer and the risk of recurrence after treatment. Liquid AI continually adapts to changing molecules, allowing for adaptive interpretation of ctDNA signals throughout disease stages and patient follow-up. The framework can enhance sensitivity in early detection, minimize the need for invasive tissue diagnosis, and facilitate individual surveillance plans. In summary, liquid AI-powered ctDNA analysis shows promise for precision oncology in the management of lung cancer, including early diagnosis, minimal residual disease monitoring, and prediction of relapse.