In a recent article published in the journal Cancer Discovery, researchers from Johns Hopkins University Kimmel Cancer Center introduced a novel blood test known as deoxyribonucleic acid (DNA) evaluation of fragments for early interception (DELFI). This innovative test utilizes artificial intelligence (AI) to detect patterns of DNA fragments linked with lung cancer. Their objective was to facilitate earlier identification of lung cancer and boost screening rates.
Study: AI-Powered Blood Test for Early Lung Cancer Detection. Image Credit: THICHA SATAPITANON/Shutterstock.com
Background
Lung cancer presents a significant global health challenge, often proving fatal if it is not detected early. Despite the potential for a cure through early identification, current screening methods, such as low-dose computed tomography (CT) scans, face obstacles due to cost, inconvenience, and radiation exposure.
DNA, the fundamental genetic material, holds the instructions for life. In healthy cells, the DNA is arranged, resembling a tightly wound ball of cotton. However, cancer cells exhibit disorganized DNA. Upon cell death, both healthy and cancerous cells release DNA fragments into the bloodstream, offering potential insights into cancer presence.
Conventional DNA analysis methods, like sequencing, are costly, complex, and require substantial DNA quantities, posing challenges, especially in early-stage cancers. Therefore, there is an urgent need for a more accessible and sensitive approach to detecting cancer cell DNA fragments.
About the Research
In this study, the authors developed DELFI, a computational method that uses AI to learn the fragmentation patterns of cell-free DNA from different sources. Their main objective was to compare the DNA fragmentation patterns of individuals with and without cancer to identify those more likely to have lung cancer. The algorithm analyzes the size, shape, and distribution of DNA fragments in the blood and compares them with those from healthy individuals and patients with lung cancer. The test generates a score indicating the likelihood of having lung cancer.
The study enrolled about 1,000 participants who met the eligibility criteria for traditional lung cancer screening using low-dose CT scans. The participants were recruited from 47 centers in 23 US states. They provided blood samples, which were analyzed by DELFI, and underwent CT scans read by radiologists. Clinical and demographic data such as age, sex, smoking history, and lung cancer risk score were also collected. Participants were monitored for a period of up to two years to verify diagnoses of lung cancer.
To train and validate the DELFI method, the authors utilized two groups of participants. The training group consisted of 576 individuals, including 164 with lung cancer and 412 without, whereas the validation group included 382 participants, of whom 104 had lung cancer, and 278 did not. Furthermore, AI was employed to learn the DNA fragmentation patterns from the training group, and then the method was applied to the validation group to test its accuracy and performance.
Research Findings
The outcomes demonstrated that the DELFI test could effectively identify individuals more likely to have lung cancer based on the patterns of DNA fragments in their blood. The test exhibited a high negative predictive value (NPV) of 99.8 %, indicating that only 2 in 1,000 individuals with a negative test result would actually have lung cancer. Additionally, the test showcased a high positive predictive value (PPV) of 83.3%, meaning that 83.3 % of individuals with a positive test result did indeed have lung cancer.
In a comparative analysis, the DELFI test outperformed another blood test measuring carcinoembryonic antigen (CEA) levels, which are often elevated in lung cancer. DELFI demonstrated superior accuracy and consistency, having a higher NPV of 99.8 % compared to the CEA test's 97.5 %. Similarly, DELFI's PPV of 83.3 % surpassed the CEA test's PPV of 18.8 %.
Furthermore, computer simulations were employed to assess the potential impact of DELFI on lung cancer screening and mortality. Various scenarios incorporating different screening uptake and follow-up rates were compared to estimate the number of cancers detected, stage distribution, and potential lives saved.
Simulations suggested that increasing DELFI's screening rate to 50 % within five years could quadruple the number of detected lung cancer cases and increase early-stage diagnoses by about 10 %. Such improvements could potentially prevent approximately 14,000 cancer deaths over five years.
Conclusion
In summary, the novel blood test proved to be effective for lung cancer detection and management. It was simple and non-invasive, potentially increasing lung cancer screening rates and reducing mortality.
Additionally, it could be used to monitor disease progression and outcomes and could be applied to other types of cancers, such as breast, colorectal, and ovarian cancers, as well as diseases that alter DNA fragmentation patterns in the blood. Moving forward, the researchers planned to seek regulatory approval for the test and conduct further studies to evaluate its clinical utility and cost-effectiveness.
Journal Reference
Artificial Intelligence Blood Test Provides a Reliable Way to Identify Lung Cancer, https://www.hopkinsmedicine.org/news/newsroom/news-releases/2024/06/artificial-intelligence-blood-test-provides-a-reliable-way-to-identify-lung-cancer, Accessed on 12- June- 2024.
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