Vysioneer employs artificial intelligence to analyze tumor heterogeneity by extracting lesion-level features and predicting treatment outcomes, enabling more accurate assessments of treatment efficacy. This technology allows for smaller oncology trials, facilitating faster confirmation of treatment results and improving patient care in cancer medicine.
Funding
$2.8M 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
Cancer treatment efficacy is difficult to assess due to tumor heterogeneity, where some lesions shrink, others remain stable, and some grow during treatment. Current response evaluation criteria do not fully capture this lesion-level variability, potentially leading to inaccurate assessments of treatment effectiveness and hindering personalized cancer medicine.
Solution
Vysioneer offers an AI-powered platform that analyzes tumor heterogeneity by extracting lesion-level features from medical images to predict treatment outcomes. The platform automates the extraction of comprehensive lesion features, going beyond traditional response evaluation criteria. By incorporating all lesions in the analysis, Vysioneer enables faster confirmation of treatment results with smaller sample sizes in oncology trials. The AI prediction capabilities explore lesion-level biomarkers to improve the accuracy of treatment outcome predictions.
Target Audience
The primary target audience includes pharmaceutical and biotech companies, as well as healthcare providers involved in oncology clinical trials and cancer treatment.
Features
- AI-driven analysis of lesion-level features to quantify tumor heterogeneity.
- Automated extraction of lesion features beyond RECIST criteria.
- Predictive modeling of treatment outcomes based on lesion-level biomarkers.
- Streamlined oncology trials with smaller sample sizes.
- FDA-cleared AI platform for automated detection and contouring of metastatic brain tumors.
- Integration with stereotactic radiosurgery workflows.