Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
Overview
- Phase
- Not Applicable
- Intervention
- AI contouring
- Conditions
- Lung Cancer
- Sponsor
- Tianjin Medical University Cancer Institute and Hospital
- Enrollment
- 500
- Locations
- 1
- Primary Endpoint
- Contouring time (min)
- Status
- Completed
- Last Updated
- 2 months ago
Overview
Brief Summary
The goal of this clinical trial is to evaluate performance and clinical applicability of AI-assisted radiotherapy contouring software (iCurveE) for thoracic organs at risk. The main question it aims to answer is:
• Does AI-assisted contouring (AI contouring with manual modification) offer greater accuracy and time efficiency compared to manual contouring? After screening, the qualified participants' thoracic CT images will be anonymized and segmented using three methods: manual, AI (AI-only), and AI-assisted contouring. The researchers will compare the results generated by the three different contouring methods with the ground truth established by expert consensus, in order to evaluate both accuracy and time-related parameters
Investigators
Zhiyong Yuan
Head of Radiation Oncology
Tianjin Medical University Cancer Institute and Hospital
Eligibility Criteria
Inclusion Criteria
- •≥18 years old, no gender limit.
- •Patients diagnosed with breast cancer, lung cancer, or esophageal cancer, who are scheduled for chest CT scanning followed by thoracic radiotherapy.
- •CT slice thickness ≤5mm.
- •Patients understand the goal of the trial, are willing to attend the trial and sign the informed consent.
Exclusion Criteria
- •Congenital malformations or abnormal anatomical structures resulting from non-tumor factors in the scan area.
- •Artifact, prosthesis or implantation causing images undistinguishable.
- •CT images not conforming to DICOM standards.
- •Investigators consider not suitable.
Arms & Interventions
AI contouring
AI contouring refers to the auto-segmentation results generated by the Res-SE Net model, with the model integrated into the auto-segmentation software (iCurveE).
AI-assisted contouring
After generating the AI contouring results, investigators will import them into the contouring platform and perform manual modifications, producing the AI-assisted contouring.
Independent manual contouring
Manual contouring refers to physicians using the brush tool on the contouring platform to segment thoracic organs at risk manually, without the use of auto-segmentation tools.
Outcomes
Primary Outcomes
Contouring time (min)
Time Frame: Within 6 months after enrollment
Manual contouring time is recorded from the time the CT is loaded on the contouring platform to the completion of contouring. AI-assisted contouring time is defined as the sum of the auto-segmentation model runtime, the transfer to the contouring platform, and the subsequent manual modification.
volumetric DICE similarity coefficient, vDSC
Time Frame: Within 6 months after enrollment
vDSC= 2×（A∩B）/(A+B), where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour.
Secondary Outcomes
- Rate of time efficiency improvement(Within 6 months after enrollment)
- Volumetric revision index, VRI(Within 6 months after enrollment)
- Recall, Rec(Within 6 months after enrollment)
- Precision, Pre(Within 6 months after enrollment)
- Investigators satisfaction score for AI contouring(Within 6 months after enrollment)
- 95th percentile Hausdorff Distance, HD95(Within 6 months after enrollment)
- Surface DICE similarity coefficient, sDSC(Within 6 months after enrollment)
- Relative volume difference, RVD(Within 6 months after enrollment)