Automated TNM Staging & Survival Prediction in Lung Cancer
This project combines CT/PET-based multi-modal data in lung cancer management to produce segmentation, benign-malignant characterization, automated TNM staging, and survival prediction. A system architecture is being developed that can be integrated into clinical workflows and brought to market. The goal is to build a reliable platform that reduces dependence on specialists while enabling faster, more consistent decision support.
Key Features
Multi-Modal CT/PET Analysis
Automated Tumor & Lymph Node Segmentation
Explainable Rule-Based TNM Staging
Personalized Survival Prediction
Why Choose This Solution?
Our Automated TNM Staging & Survival Prediction in Lung Cancer solution provides key advantages to healthcare institutions, improving patient care quality while optimizing operational efficiency.
- Faster, More Consistent Clinical Assessment
- Reduced Workload for Specialists
- Data-Driven Treatment Planning
- Transparency in Clinical Decision-Making
Oncology & Radiology Units
University Hospitals
Clinical Research Centers
Digital Health Decision-Support Platforms
How It Works?
Integration
Connects to your system
Data Upload
Medical data is securely transferred
AI Analysis
AI performs the analysis
Result
Detailed report is provided
Frequently Asked Questions about Automated TNM Staging & Survival Prediction in Lung Cancer
Answers to the most common questions about our Automated TNM Staging & Survival Prediction in Lung Cancer solution.
It provides end-to-end decision support, from segmentation outputs to lesion characterization, automated staging, and prognosis.
Yes — the aim is seamless incorporation via DICOM/PACS-compatible integration with clinical processes.
Because components are currently in development and prototyping, the project is being run as active R&D.