Breast Cancer, Esophageal Cancer, Lung Cancer
Conditions
Keywords
Artificial Intelligence, Radiotherapy, contouring, thoracic organs at risk
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
Interventions
None listed
Sponsors
Tianjin Medical University Cancer Institute and Hospital
Guangzhou Perception Vision Medical Technology Co. Ltd
People's Hospital of Guangxi Zhuang Autonomous Region
Shanxi Province Cancer Hospital
Fifth Affiliated Hospital, Sun Yat-Sen University
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| volumetric DICE similarity coefficient, vDSC | 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. |
| Contouring time (min) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 95th percentile Hausdorff Distance, HD95 | Within 6 months after enrollment | 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 |
| Surface DICE similarity coefficient, sDSC | Within 6 months after enrollment | 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 |
| Rate of time efficiency improvement | Within 6 months after enrollment | Rate of efficiency time improvement= (manual contouring duration - AI-assisted contouring duration)/ manual contouring duration\*100% |
| Volumetric revision index, VRI | Within 6 months after enrollment | 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. |
| Recall, Rec | Within 6 months after enrollment | 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. |
| Precision, Pre | Within 6 months after enrollment | 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. |
| Relative volume difference, RVD | Within 6 months after enrollment | 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. |
| Investigators satisfaction score for AI contouring | Within 6 months after enrollment | Evaluated on a 1-5 Likert scale: 1 - strongly dissatisfied, 2 - dissatisfied, 3 - neutral, 4 - satisfied, 5 - strongly satisfied. |
Countries
China
Contacts
PRINCIPAL_INVESTIGATORZhiyong Yuan, Ph.D.
Tianjin Medical University Cancer Institute and Hospital
Outcome results
None listed