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| Name | Class |
|---|---|
| Guangzhou Perception Vision Medical Technology Co. Ltd | UNKNOWN |
| People's Hospital of Guangxi Zhuang Autonomous Region | OTHER |
| Shanxi Province Cancer Hospital | OTHER |
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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
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. | ||
| 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. |
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| Measure | Description | Time Frame |
|---|---|---|
| volumetric DICE similarity coefficient, vDSC | 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. | Within 6 months after enrollment |
| Contouring time (min) | 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. | Within 6 months after enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| 95th percentile Hausdorff Distance, HD95 | HD95(A, B) = max (h95(A, B), h95(B, A)), where h95(A, B) is the 95th percentile of the shortest distances from all points on surface A to surface B, and vice-versa for h95(B, A). A represents the ground truth and B represents the manual, AI or AI-assisted delineation | Within 6 months after enrollment |
| Measure | Description | Time Frame |
|---|---|---|
| Number of adverse events, AEs | Participant Adverse events during CT scanning | Within 1 day after CT scanning |
| Number of device defects during AI-assisted contouring | Number of failures in generating, transferring, or saving auto-segmentation results |
Inclusion Criteria:
Exclusion Criteria:
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This trial will enroll 500 patients with lung, esophageal, or breast cancer, who are scheduled to receive thoracic radiotherapy across five clinical cancer institutes.
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| Name | Affiliation | Role |
|---|---|---|
| Zhiyong Yuan, Ph.D. | Tianjin Medical University Cancer Institute and Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tianjin Medical University Cancer Institute and Hospital, Tianjin Key Laboratory of Cancer Prevention and Therapy | Tianjin | Tianjin Municipality | 300060 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41917034 | Derived | Niu 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. |
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The protocol of this study are available from the corresponding author upon reasonable request after the manuscript publication.
Beginning 1 year after publication with no end date.
Requests must include a detailed protocol, analysis plan, and data exchange with institutional approvals in place before data transfer of any information.
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D001943 | Breast Neoplasms |
| D004938 | Esophageal Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| Fifth Affiliated Hospital, Sun Yat-Sen University |
| OTHER |
| Union Hospital, Tongji Medical College, Huazhong University of Science and Technology | OTHER |
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| Surface DICE similarity coefficient, sDSC | sDSC = (|S(A) ∩ S(B)τ| + |S(B) ∩ S(A)τ|) / (|S(A)| + |S(B)|), where S(A) and S(B) are the sets of points on the surfaces of A and B, S(B)τ represents the points on surface B that are within the tolerance τ of surface A, and S(A)τ represents the points on surface A that are within the tolerance τ of surface B. A represents the ground truth and B represents the manual, AI or AI-assisted delineation | Within 6 months after enrollment |
| Rate of time efficiency improvement | Rate of efficiency time improvement= (manual contouring duration - AI-assisted contouring duration)/ manual contouring duration*100% | Within 6 months after enrollment |
| Volumetric revision index, VRI | VRI = [(A- A∩B) + (B- A∩B)] /A, where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour. | Within 6 months after enrollment |
| Recall, Rec | Rec = | A∩B| / A, where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour. | Within 6 months after enrollment |
| Precision, Pre | Pre= |A∩B| / B, where A refers to the volume of the ground truth, and B refers to the volume of manual, AI, or AI-assisted contour. | Within 6 months after enrollment |
| Relative volume difference, RVD | RVD = |A-B| /A, where A refers to the volume of the ground truth, and B refers to the volume of the manual, AI, or AI-assisted contour. | Within 6 months after enrollment |
| Investigators satisfaction score for AI contouring | Evaluated on a 1-5 Likert scale: 1 - strongly dissatisfied, 2 - dissatisfied, 3 - neutral, 4 - satisfied, 5 - strongly satisfied. | Within 6 months after enrollment |
| Within 6 months after enrollment |
| D008171 |
| Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D006258 | Head and Neck Neoplasms |
| D004066 | Digestive System Diseases |
| D004935 | Esophageal Diseases |
| D005767 | Gastrointestinal Diseases |