Scientists say they have devised a way to screen for prostate cancer using a drop of urine, a sensor, and AI algorithms. And the test takes just twenty minutes, and is 99 per cent accurate, according to results from a small-scale test.
The risk of developing prostate cancer increases for men as they get older, and the over 50s are supposed to be routinely screened for the disease. If the examination – performed either with a blood test or something... more hands on – suggests there's a problem, the patient may undergo a biopsy, where a small amount of tissue is extracted from the prostate to confirm whether or not there are cancerous cells present. But these screening tests tend to generate a lot of false positives, leading to unnecessary biopsies.
A team of researchers led by the Korea Institute of Science and Technology (KIST) believe that prostate cancer can be screened non-invasively and quickly using just a urine sample. If high levels of PCA3 are detected, there’s a high chance of prostate cancer.
Urine tests, however, can also be inaccurate at predicting whether someone really needs a biopsy or not. Instead of looking for just PCA3, the team uses a biosensor made up of a dual-gate field effect transistor to sense the presence of biomarkers in the urine. These biomarkers cause a shift in the transistor’s reference voltage.
By measuring this change after a tiny drop of urine is placed on the biosensor’s surface, scientists can determine the concentration of the biomarkers in the fluid. Trained algorithms can take these concentration readings, and use them to predict whether or not someone has the disease.
“Basically, a biomarker is a certain substance in our body where its concentration is affected by a specific disease state,” Kwanhyi Lee – co-author of a paper describing the technology, published in ACS Nano, and a principal research scientist at KIST – explained to The Register on Tuesday.
“In our study, we chose four different biomarkers related to prostate cancer. Simply speaking, we see either an increase or decrease of biomarker concentration from cancer patients compared to healthy individuals.
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“In a single biomarker based diagnosis, we can simply set the threshold to diagnose the disease. However, when there are more biomarkers, four in our case, understanding the relation between biomarker data and disease state is not easy. Considering the potential of multiple biomarker based diagnosis, it is necessary to analyze multiple biomarker data. Here, we utilized AI algorithms to learn the pattern of biomarker data so that the AI algorithm predicts the cancer."
The algorithms were trained to look for specific patterns in the biomarker data that are common in patients with prostate cancer. If they detect the same patterns in a fresh urine sample, there’s a high chance of the disease. The team collected 76 urine samples from a mixture of healthy patients and men with prostate cancer, and used 70 per cent of them to train the algorithms and 30 per cent for testing.
Initial results show that the algorithms were able to correctly predict with at least 99 per cent accuracy whether someone had prostate cancer or not. It should be noted, however, that only 23 people were tested in the experiment so the results of this limited experiment should be taken with a pinch of salt.
After a further development of the technology, I believe that replacing the current blood test will be possible
Lee said that although the false positive and negative rates of the algorithms were low at 0.024 and 0.037 respectively, the team needed to verify their results with many more patients.
“After a further development of the technology, I believe that replacing the current blood test will be possible," he said. "To make this happen, there are a few challenges we need to overcome. First, a validation of our approach with a much larger data set should be checked. Second, miniaturization of the sensor is needed.
“And then we need to closely work with healthcare experts on fields to monitor the performance of our sensing platform to determine replacing the current blood-based test. Personally, I think that making the sensing platform more affordable without compromising the performance is the key.”
At the moment, Lee believes the test is not yet good enough to completely eradicate the need for further biopsy tests. He hopes that one day in the future the team will develop a biosensor capable of detecting multiple biomarkers for different cancers that can be analysed with AI algorithms to diagnose patients. ®