QOCA® image Smart RT Contouring System
Overview
QOCA® image Smart RT Contouring System is an AI-powered post-processing software designed to automatically outline organs at risk (OAR) on CT scans. It utilizes deep-learning-based algorithms to process DICOM CT imaging data and generate RTSTRUCT objects. This system is intended for use in external beam radiation therapy treatment planning, providing accurate organ contours as input data for radiation oncology clinicians. The software aims to improve workflow efficiency and precision in treatment delivery by automating the contouring process. It operates via a web-based interface for managing settings and progress and supports multi-detector CT scans, with a recommendation for scanners with more than 16 slices for radiation therapy simulation CT. The system is designed for adult use and is intended for the head, neck, and pelvis regions.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- AI-powered post-processing software
- Automated contouring of organs at risk (OAR) on CT scans
- Utilizes deep-learning-based algorithms
- Generates RTSTRUCT objects for radiation oncology use
- Web-based interface for settings and progress management
- Supports multi-detector CT scans (recommended >16 slices)
- DICOM compliant output
- Aids in efficient treatment plan preparation
- Improves workflow and precision in treatment delivery
- Intended for head, neck, and pelvis regions
Use Cases
- External beam radiation therapy treatment planning
- Automated contouring of organs at risk (OAR)
- Input data for radiation therapy treatment planning systems
- Reducing contouring time in RT workflow
- Improving consistency and quality in contouring
What Physicians Need to Know
Always review and, if necessary, manually edit AI-generated contours before final approval and use in treatment planning, as the system is intended to provide a starting point. Pay close attention to challenging OARs that may require more refinement. Leverage the time savings from automated contouring to focus on complex cases and other critical aspects of patient care. Ensure seamless integration with your existing Treatment Planning System for an efficient workflow.
The QOCA® image Smart RT Contouring System is designed to be a post-processing software that integrates into existing radiation oncology workflows. It requires connection with DICOM-compliant Treatment Planning Systems (TPS) and interactive contouring applications for the essential steps of reviewing, editing, and accepting the automatically generated contours. The system outputs DICOM RTSTRUCT objects, which are a standard format for transferring contoured structures to other systems. It supports DICOM 3.0 compliance, facilitating interoperability with various medical imaging devices and systems.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Unknown |
| Deployment | Web-based interface. |
| Data Export | Unknown |
| Compliance | |
| BAA Available | Unknown |
| HIPAA Compliant | Unknown |
| FDA Status | Cleared AI-estimated |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Radiology |
What the Web Says
The QOCA® image Smart RT Contouring System is an AI-powered solution designed to automate the delineation of structures on CT imaging for radiotherapy planning. It utilizes deep-learning algorithms to generate contours, aiming to reduce the time-consuming nature of manual contouring and improve consistency. The system received FDA clearance on February 13, 2024.
Overall: PositiveStrengths
- Automates contouring of DICOM CT imaging data using deep-learning algorithms.
- Aims to save time in radiotherapy planning by reducing manual delineation.
- Potential to improve consistency in contouring.
- FDA-cleared, providing a concrete basis for vendor evaluation.
Limitations
- AI-generated contours still require clinical review and editing.
- Specific structures, such as pharyngeal constrictors, larynx, and submandibular glands, may consistently require more time-consuming edits.
- Performance can vary across different anatomical sites and specific organs at risk.
- Integration with existing clinical workflows can be a barrier to adoption.
Based on reviews from: mdpi.com, rtmedical.com.br, gitnux.org, synaptiq.io, nih.gov, parikhradiology.com, ekipa.ai
Last updated: 2026-10-31
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