AI-Driven Liquid Biopsy For Ultra-Early Cancer Detection And Precision Oncology

Abstract

Background: In the global society, cancer has been one of the leading causes of death, and early diagnosis is crucial in increasing the survival and treatment rates of patients. Traditional tissue biopsies are destructive, time-consuming, and lack the ability for continuous follow-up on the progression of the tumors. Based on aiding artificial intelligence, liquid biopsy has become a new technology that integrates artificial intelligence with extremely minimal diagnostic procedures to aid ultra-early cancer diagnosis and precision oncology.

Objective: The main aim of the study was to explore the effectiveness, adoption, and perceived value of AI-based liquid biopsy in ultra-early cancer prevention and precision oncology. The other problem that drew the attention of the researchers was to explore the awareness, trust, and perception of those who were studied on the adoption and implementation of artificial intelligence in the diagnosis and personalization of cancer diagnostics and treatment.

Methodology: This research employed a quantitative research design. A structured questionnaire was used to collect primary data and was mailed to all 253 respondents who satisfied the following criteria: they had a particular educational and professional background, including students, healthcare practitioners, researchers, and IT/AI professionals. The data collected were tested using statistical tests such as t-test (independent Samples), ANOVA (one-way), Kruskal-Wallis test, Chi-square test, Pearson correlation analysis, and regression analysis to test the collected data as a normality test, Reliability test, and validity tests (KMO and Bartlett's test).

Results: The findings revealed that the respondents were inclined positively to consider AI-driven technologies (liquid biopsy) based on their findings. The Normality Test showed that the data were normally distributed, and the Reliability Test showed that an excellent internal consistency existed with a high Cronbach's alpha of above 0.90. Construct validity, as suggested by the Validity Test, was seen to be satisfactory in terms of significant KMO and Bartlett outcomes of the test. The inference statistics tests have proved that the level of demographic differences and relationships was significant. Moreover, Pearson Correlation and Regression Analyses showed a positive correlation among trust in the AI systems, the knowledge about the liquid biopsy, the accuracy of the diagnosis, and the perception of the personal treatment technologies based on cancer.

Conclusion: The researchers end the article by stating that AI-based liquid biopsy has potential in regard to cancer diagnostics and precision oncology, as it will not only allow cancer to be diagnosed earlier but also can now lead to fewer invasive procedures and provide a more personalized approach to the treatment. This aspect can contribute a lot to efficacy and outcomes in the medical field by the introduction of liquid biopsy technology and the application of artificial intelligence in the management of liquid biopsies to take care of patients. However, issues surrounding cost, privacy, and access to derail quality and ethical adoption of AI-based healthcare systems should be minimized around the world.