Clinical trial · Observational
Clinical Validation of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
| Esophageal Cancer | Malignant Esophageal Neoplasm | CURATED_EXACT | 0.92 |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (3)
- label
- Independent manual contouring
- description
- 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.
- label
- AI contouring
- description
- 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).
- label
- AI-assisted contouring
- description
- After generating the AI contouring results, investigators will import them into the contouring platform and perform manual modifications, producing the AI-assisted contouring.
Primary outcomes (2)
- measure
- volumetric DICE similarity coefficient, vDSC
- timeFrame
- Within 6 months after enrollment
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: 1. ≥18 years old, no gender limit. 2. Patients diagnosed with breast cancer, lung cancer, or esophageal cancer, who are scheduled for chest CT scanning followed by thoracic radiotherapy. 3. CT slice thickness ≤5mm. 4. Patients understand the goal of the trial, are willing to attend the trial and sign the informed consent. Exclusion Criteria: 1. Congenital malformations or abnormal anatomical structures resulting from non-tumor factors in the scan area. 2. Artifact, prosthesis or implantation causing images undistinguishable. 3. CT images not conforming to DICOM standards. 4. Investigators consider not suitable.
References
Publications (1)
- DERIVEDNiu G, Guan Y, Zhang Y, Song Y, Yan M, Li S, Liu T, Huang S, Chen J, Wang X, Zhang W, Meng M, Liu Y, Chen J, Fu Y, Zhao D, Huang J, Yang K, Cao J, Yuan H, Guo S, Pei X, Wu D, Nan Y, Yan Z, Lu Y, Zhao L, Yuan Z. A prospective multicenter trial of deep learning auto-segmentation for organs at risk in thoracic radiotherapy. Nat Commun. 2026 Mar 31;17(1):4633. doi: 10.1038/s41467-026-70863-9. PMID 41917034