Kim, Hojun Park, Sungwook Jeong, In Gab Song, Sang Hoon Jeong, Youngdo Kim, Choung-Soo Lee, Kwan Hyi 2024-01-19T15:03:58Z 2024-01-19T15:03:58Z 2021-09-05 2021-03 1936-0851 https://pubs.kist.re.kr/handle/201004/117251 Screening for prostate cancer relies on the serum prostatespecific 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. English American Chemical Society Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis Article 10.1021/acsnano.0c06946 1 ACS Nano, v.15, no.3, pp.4054 - 4065 ACS Nano 15 3 4054 4065 N scie scopus 000634569100028 2-s2.0-85097732503 Chemistry, Multidisciplinary Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article TECHNOLOGY BIOMARKERS PLATFORM BIOPSY MARKER DIAGNOSIS IMMUNOASSAY urine multimarker artificial intelligence prostate cancer cancer screening