English
Learn about the science behind miR’s breakthrough prostate and bladder cancer diagnosis, classification, and monitoring technology that gives healthcare providers, patients, and payors the greatest confidence in test results and care plans.
PSA False Positive Rate has
been reported at up to 75%
Unlike other available blood and urine tests, the miR platform produces a molecular characterization of the likelihood of malignant progression, allowing for far more accurate results.
We capture and lyse exosomes from the patient’s urine sample, then interrogate the extracted sncRNA in a high-throughput OpenArrayTM system. Our proprietary algorithm calculates patient results and provides clear Yes/No cancer diagnosis and High/Intermediate/Low classification.
Our unique process provides actionable data to providers far more quickly than today’s standard of care, rapidly accelerating the timeline to treatment or monitoring, if necessary.
A high throughput OpenArray™ system is used to interrogate sncRNA entities of interest.
A urine sample is provided. No Digital Recal Exam required
A standard RNA extraction method is used for prostate, bladder, and renal cancers: Exosomes in cell-free urine are captured and lysed to enable RNA extraction
miR’s unique sncRNA extraction and interrogation techniques coupled with an advanced, proprietary algorithm allow physicians to confidently diagnose and classify prostate cancers with just one urine sample, eliminating unnecessary treatments for prostate cancer patients
Multiple peer-reviewed journals and validation studies have shown the extraordinary sensitivity and specificity of miR’s molecular diagnostic prostate cancer testing and monitoring technology.
miR is the first peer-reviewed liquid biopsy urine test that accurately detects, classifies, and monitors prostate cancer.
Published clinical validation studies prove that the miR cancer testing and classification platform can achieve an 80% reduction in unnecessary prostate biopsies through more accurate cancer detection and aggression classification.
