Cancer can be precisely diagnosed using a urine test with artificial intelligence

Authored by eurekalert.org and submitted by mvea

Successful precision cancer diagnosis through an AI analysis of multiple factors of prostate cancer. Potential application of the precise diagnoses of other cancers by utilizing a urine test

Prostate cancer is one of the most common cancers among men. Patients are determined to have prostate cancer primarily based on *PSA, a cancer factor in blood. However, as diagnostic accuracy is as low as 30%, a considerable number of patients undergo additional invasive biopsy and thus suffer from resultant side effects, such as bleeding and pains.

*Prostate-Specific Antigen (PSA): a prostate-specific antigen (a cancer factor) used as an index for the screening of prostate cancer.

The Korea Institute of Science and Technology (KIST) announced that the collaborative research team led by Dr. Kwan Hyi Lee from the Biomaterials Research Center and Professor In Gab Jeong from Asan Medical Center developed a technique for diagnosing prostate cancer from urine within only twenty minutes with almost 100% accuracy. The research team developed this technique by introducing a smart AI analysis method to an electrical-signal-based ultrasensitive biosensor.

As a noninvasive method, a diagnostic test utilizing urine is convenient for patients and does not need invasive biopsy, thereby diagnosing cancer without side effects. However, as the concentration of cancer **factors is low in urine, a urine-based biosensor has been utilized for classifying risk groups rather than for precise diagnosis thus far.

**Cancer Factor: a cancer-related biological index that can measure and evaluate drug reactivity objectively for a normal biological process, disease progress, and a treatment method.

Dr. Lee's team at the KIST has been working toward developing a technique for diagnosing disease from urine by utilizing the electrical-signal-based ultrasensitive biosensor. An approach utilizing a single cancer factor associated with a cancer diagnosis was limited in increasing the diagnosis accuracy to over 90%. However, to overcome this limitation, the team simultaneously utilized different kinds of cancer factors instead of using only one to enhance the diagnostic accuracy innovatively.

The team developed an ultrasensitive semiconductor sensor system capable of simultaneously measuring trace amounts of selected four cancer factors in urine for diagnosing prostate cancer. They trained AI by using the correlation between the four cancer factors, which were obtained from the developed sensor. The trained AI algorithm was then used to identify those with prostate cancer by analyzing complex patterns of the detected signals. The diagnosis of prostate cancer by utilizing the AI analysis successfully detected 76 urinary samples with almost 100 percent accuracy.

"For patients who need surgery and/or treatments, cancer will be diagnosed with high accuracy by utilizing urine to minimize unnecessary biopsy and treatments, which can dramatically reduce medical costs and medical staff's fatigue," Professor Jeong at Asan Medical Center said. "This research developed a smart biosensor that can rapidly diagnose prostate cancer with almost 100 percent accuracy only through a urine test, and it can be further utilized in the precise diagnoses of other cancers using a urine test," Dr. Lee at the KIST said.

This research was supported by the Korean National Research Foundation's Midcareer Researcher Grant program, governmental departments(the Ministry of Science and ICT, the Ministry of Trade and Industry, the Ministry of Health and Welfare, and the Ministry of Food and Drug Safety), and Korea Medical Device Development Fund, funded by the Ministry of Science and ICT (MSIT). The research results have been published in the latest issue of ACS Nano, a top international academic journal in the nano-field.

Hiltaku on January 21st, 2021 at 12:45 UTC »

What stage does the cancer need to be in for this test to pick it up?

tdgros on January 21st, 2021 at 12:40 UTC »

They get >99% on 76 specimens only, how does that happen?

I can't access the paper, so I don't really know on how much samples they validated their ML training. Does someone have the info?

edit: lots of people have answered, thank you to all of you! See this post for lots of details: https://www.reddit.com/r/science/comments/l1work/korean_scientists_developed_a_technique_for/gk2hsxo?utm_source=share&utm_medium=web2x&context=3

edit 2: the post I linked to was deleted because it was apparently false. sorry about that.

mvea on January 21st, 2021 at 11:29 UTC »

The post title is from the linked academic press release here:

The Korea Institute of Science and Technology (KIST) announced that the collaborative research team led by Dr. Kwan Hyi Lee from the Biomaterials Research Center and Professor In Gab Jeong from Asan Medical Center developed a technique for diagnosing prostate cancer from urine within only twenty minutes with almost 100% accuracy. The research team developed this technique by introducing a smart AI analysis method to an electrical-signal-based ultrasensitive biosensor.

As a noninvasive method, a diagnostic test utilizing urine is convenient for patients and does not need invasive biopsy, thereby diagnosing cancer without side effects.

This research developed a smart biosensor that can rapidly diagnose prostate cancer with almost 100 percent accuracy only through a urine test, and it can be further utilized in the precise diagnoses of other cancers using a urine test.

The source journal article is here:

https://pubs.acs.org/doi/10.1021/acsnano.0c06946

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis

Hojun Kim, Sungwook Park, In Gab Jeong, Sang Hoon Song, Youngdo Jeong, Choung-Soo Kim, and Kwan Hyi Lee

ACS Nano 2020, XXXX, XXX, XXX-XXX

Publication Date:December 9, 2020

DOI: https://doi.org/10.1021/acsnano.0c06946

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.