Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis
Abstract
Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of the test, there is a critical unmet need of precision screening for prostate cancer. Here, we introduced a urinary multimarker biosensor with a capacity to learn to achieve this goal. The correlation of clinical state with the sensing signals from urinary multimarkers was analyzed by two common machine learning algorithms. As the number of biomarkers was increased, both algorithms provided a monotonic increase in screening performance. Under the best combination of biomarkers, the machine learning algorithms screened prostate cancer patients with more than 99% accuracy using 76 urine specimens. Urinary multimarker biosensor leveraged by machine learning analysis can be an important strategy of precision screening for cancers using a drop of bodily fluid.
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AI-based wearable biosensors were used for cancer detection, such as using a dual-gate FET to detect prostate cancer in urinary samples with multiple biomarkers.
“AI-based wearable biosensors were used for cancer detection, such as using a dual-gate FET to detect prostate cancer in urinary samples with multiple biomarkers.”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [203]
With random forest and neural network algorithms, screening performance was improved with more than 99% accuracy using surface-enhanced Raman spectroscopy signals of 76 urine specimens for multiple biomarkers' detection, including ANXA3, ENG, PSMA, and ERG.
“With the help of random forest (RF) and neural network algorithms, the screening performance was improved with more than 99% accuracy using surface-enhanced Raman spectroscopy signals of 76 urine specimens for multiple biomarkers’ detection, including ANXA3, ENG, PSMA, and ERG [203].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [203]
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- Authors
- Kim, H., Park, S., Jeong, I. G., Song, S. H., Jeong, Y., Kim, C.-S., & Lee, K. H.
- Affiliations
- Korea Institute of Science and Technology (KR)
- Asan Medical Center (KR)
- Korea University (KR)
- Corresponding author
- Lee, Kwan Hyi
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-12-09
- Volume
- 15
- Issue
- 3
- Pages
- 4054-4065
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3110857070
- Cited by
- 130
- References
- 47
- Citations by year
- 2026: 29 · 2025: 32 · 2024: 23 · 2023: 21 · 2022: 12 · 2021: 13
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- ACS Nano (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Health and Welfare (202012D07)
- Ministry of Food and Drug Safety (202012D07)
- Ministry of Science and ICT, South Korea (202012D07)
- National Research Foundation of Korea (2020R1A2C2009291)
- Ministry of Trade, Industry and Energy (202012D07)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced biosensing and bioanalysis techniques
- Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Prostate Cancer Diagnosis and Treatment
- Keywords
- prostate cancer, non-invasive cancer screening, urinary biomarkers, artificial intelligence, machine learning, prostate-specific antigen, false positives, urine samples
- MeSH terms
- Artificial Intelligence, Biopsy, Humans, Male, Prostatic Neoplasms, Biomarkers, Tumor, Prostate-Specific Antigen, Early Detection of Cancer
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