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
This AI tool is a post-processing aid for OAR contouring, not a standalone diagnostic or treatment planning system. Always perform thorough manual review, editing, and acceptance of the AI-generated contours within your Treatment Planning System before clinical use. It is specifically for adult patients and indicated for head, neck, and pelvis regions. Leverage the efficiency gains for routine contouring to focus on complex cases and critical decision-making.
The system is designed for seamless integration into existing radiation oncology workflows by accepting DICOM CT input and producing standard RTSTRUCT outputs. Ensure your current imaging infrastructure and Treatment Planning Systems are DICOM 3.0 compliant to facilitate optimal data exchange. The web-based management interface allows for flexible access and configuration.
Details
| Category | Oncology AI, Radiology & Imaging AI |
| Pricing | Unknown |
| Deployment | Web-based interface (typically cloud-based or on-premise server with web access) |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
1 AI-estimated The QOCA® image Smart RT Contouring System received FDA 510(k) clearance (K231855) on February 13, 2024, as a Class II device. It is an AI-powered post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms for external beam radiation therapy treatment planning. |
| Integrations | |
| EHR | Not specified |
| Specialties | Oncology, Radiology |
What the Web Says
The QOCAu00ae image Smart RT Contouring System is an AI-based solution designed to automate the contouring of organs at risk (OARs) and lymphatic drainage areas for head and neck cancer patients in radiation therapy. It aims to significantly reduce the manual contouring time, potentially saving 2-3 hours per patient, and improve consistency by minimizing inter-observer variability. While auto-contouring systems like QOCAu00ae image are becoming more common in radiation oncology, they still require physician review and editing to ensure accuracy, especially for target volumes and smaller, less visible structures.
Overall: PositiveStrengths
- Significantly reduces manual contouring time (2-3 hours per patient for head and neck cancer).
- Reduces inter-observer variability in contouring.
- Improves the quality of contouring for lymphatic drainage areas and organs at risk.
- Can improve efficiency and allow physicians to focus on other clinical tasks.
- Helps standardize contouring within an institution and potentially across the field.
- Generally good subjective quality of auto-contours, often requiring only minor changes.
Limitations
- Auto-contours still require physician review and editing, which can be time-consuming.
- Accuracy issues can arise, particularly with target volumes and smaller lesions.
- Performance can vary for different OARs, with some structures being more challenging for AI to contour accurately.
- May hinder residents' understanding of CT anatomy if not used in conjunction with manual contouring practice.
- Poor visibility of small lesions on non-contrast-enhanced MR-Linac images can lead to unsatisfactory target contouring accuracy.
Based on reviews from: Collections, MDPI, PubMed, RT Medical Systems
Last updated: 2026-07-19
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