A research team led by Dr. Ho Sang Jung of the Advanced Bio and Healthcare Materials Research Division at the Korea Institute of Materials Science has developed an innovative sensor material that amplifies the optical signals of cancer metabolites in body fluids (saliva, mucus, urine, etc.) and analyzes them using artificial intelligence to diagnose cancer.
This technology quickly and sensitively detects metabolites and changes in cancer patients' body fluids, providing a non-invasive way to diagnose cancer instead of traditional blood draws or biopsies. In collaboration with Professor Soo Woong Yoo of Chonnam National University Hospital, the team was able to diagnose colorectal cancer by inserting a plasmonic needle that amplifies the Raman signals of molecules into a 1-millimeter hole that can be inserted with a colonoscopy camera, and swabbing the surface of the tumor without causing bleeding in order to analyze its composition.
The team also developed a technology that collects saliva from lung cancer patients and categorizes the cancer's stage, in collaboration with Professor Byung-Ho Chung at Samsung Medical Center. The breath of lung cancer patients contains volatile organic compounds (VOCs) that are different from those of healthy individuals. These compounds dissolve in saliva and are present as lung cancer metabolites. The team has perfected a technology that uses paper-based sensors to distinguish between normal individuals and lung cancer patients, as well as to stage lung cancer using artificial intelligence.
There are many stories of dogs that barked at their owners so much that the owners thought something was wrong. As a result, they went to the doctor and discovered cancer. This is because dogs have a sensitive sense of smell that allows them to smell the metabolites, including VOCs, that exist in human body fluids. The team sought to implement these principles into a cancer diagnostic sensor. The technology detected signals from metabolites in body fluids with high sensitivity using plasmonic materials that amplify Raman signals by more than 100 million times without utilizing conventional, complex and expensive equipment. Artificial intelligence analysis and mathematical modeling calculations were used to suggest biomarkers for diagnosis.
Dr. Ho Sang Jung who is leading this project said"The developed technology can be expanded not only to diagnose cancer, but also to diseases with poorly understood diagnostics, such as synaptic diseases," and added, "We will enter the global diagnostic market based on domestic source technologies and take the lead in developing technologies that people can experience." The technology developed by the team was ranked as the No. 1 research achievement in the 'Top 10 Outstanding Research Achievements of the Year' survey conducted by KIMS last year, and they are continuing to develop innovative technology.
This work was funded by the Fundamental Research Program of KIMS and the Biomedical Technology Development Project of the National Research Foundation of Korea. The results of the study have been published in three papers and recognized for their excellence. Two articles were published in Biosensors and Bioelectronics (IF:10.7, JCR<3%), the world's leading authority on biosensors, on January 15, 2024, and August 3, 2024. Additionally, an article was published in Sensors and Actuators B-Chemical (IF:8, JCR<1%) on August 1, 2024. In addition, as a result of this research, a total of 10 patents have been filed in Korea, the U.S., and Europe for this research.
Meanwhile, the research team developed a cancer diagnosis technology using urine last year and transferred the technology to SOLUM Healthcare. The recipient company is currently working on licensing the technology to apply it to products. This year, the technology has advanced to the point where it can detect the presence of multiple cancers in urine at once. The research team analyzed urine samples from approximately 250 patients with pancreatic cancer, prostate cancer, lung cancer, and colorectal cancer at the same time. They were able to perform rapid analysis and use artificial intelligence to determine results within about 2 hours for 100 patients. The research team also explained that they achieved clinical sensitivity and specificity of over 98%.
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