Predica Diagnostics develops diagnostic assays based on proprietary ciRNAseq technology to improve cancer management. Their CervicaDX assay non-invasively assesses cervical cancer risk, aiming to prevent overdiagnosis and overtreatment in screening programs. The company provides objective, algorithm-based treatment suggestions to personalize patient care pathways.
Funding
$3.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current cervical cancer screening methods often lead to overdiagnosis and overtreatment of women with low-risk cervical abnormalities. Existing diagnostic tools lack the specificity to accurately differentiate between transient HPV infections and those likely to progress to cervical cancer. This results in unnecessary anxiety, follow-up procedures, and potential harm to patients.
Solution
Predica Diagnostics offers CervicaDX, a non-invasive assay that utilizes proprietary ciRNAseq technology to improve the accuracy of cervical cancer screening. CervicaDX analyzes cervical samples to identify specific molecular signatures associated with high-risk precancerous lesions. By providing a histology-independent molecular diagnosis, the assay helps clinicians to better predict which women will benefit from intervention and which can safely avoid unnecessary procedures. The platform aims to personalize treatment plans and reduce the incidence of overdiagnosis and overtreatment in cervical cancer screening programs.
Target Audience
The primary target audience includes women participating in cervical cancer screening programs, as well as gynecologists, pathologists, and other healthcare professionals involved in cervical cancer diagnosis and treatment.
Features
- Proprietary ciRNAseq technology for targeted RNA sequencing
- Non-invasive cervical sample collection
- Histology-independent molecular diagnosis
- Prediction of treatment response based on molecular signatures
- Identification of high-risk precancerous lesions with improved specificity
- Algorithms with proven value for objective treatment suggestions