Lung CancerNCT05787522
Clinical Validation of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
An observational study of Lung Cancer and Breast Cancer, run by Tianjin Medical University Cancer Institute and Hospital at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lung Cancer, Breast Cancer, Esophageal Cancer
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 30 Sep 2022to 6 Mar 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This trial will enroll 500 patients with lung, esophageal, or breast cancer, who are scheduled to receive thoracic radiotherapy across five clinical cancer institutes.
Who may be able to join
- ≥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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Tianjin Medical University Cancer Institute and Hospital, Tianjin Key Laboratory of Cancer Prevention and Therapy
Tianjin, Tianjin Municipality, 300060
About this study
Official title: Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
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
- Lung Cancer, Breast Cancer, Esophageal Cancer
- Keywords
- Artificial Intelligence, Radiotherapy, contouring, thoracic organs at risk
- Medical subject headings
- Lung Neoplasms, Breast Neoplasms, Esophageal Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: Within 6 months after enrollment
Secondary outcomes (8)
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
Measured: Within 6 months after enrollment
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
Measured: 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%
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Within 6 months after enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Mar 2023
- Study start30 Sep 2022
- Primary completion27 Jul 2023
- Study completion6 Mar 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Tianjin Medical University Cancer Institute and HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 500 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- 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
- Responsible party
- Sponsor
- Investigators
- Zhiyong Yuan, Ph.D., Principal investigator, Tianjin Medical University Cancer Institute and Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05787522
- Sponsor's protocol ID
- PVMEDCT202201
- Status last verified
- Feb 2026
- First submitted
- 2 Mar 2023
- Last updated
- 12 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The protocol of this study are available from the corresponding author upon reasonable request after the manuscript publication.
Publications
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.
PubMed 41917034
Study at a glance
From the registry record, last updated by the sponsor on 12 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective, Multicenter, Randomized Evaluation of the Performance and Clinical Applicability of AI-Assisted Radiotherapy Contouring Software for Thoracic Organs at Risk
- ClinicalTrials.gov ID
- NCT05787522
- Acronym / sponsor ID
- PVMEDCT202201
- Conditions
- Lung Cancer, Breast Cancer, Esophageal Cancer
- Keywords
- Artificial Intelligence, Radiotherapy, contouring, thoracic organs at risk
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Tianjin Medical University Cancer Institute and HospitalUniversity, hospital or other
- Collaborators
- 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 start
- 30 Sep 2022 (actual)
- Primary completion
- 27 Jul 2023 (actual)
- Study completion
- 6 Mar 2024 (actual)
- First posted
- 28 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This trial will enroll 500 patients with lung, esophageal, or breast cancer, who are scheduled to receive thoracic radiotherapy across five clinical cancer institutes.
Key inclusion criteria (4 listed)
- ≥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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
8 secondary outcomes
95th percentile Hausdorff Distance, HD95
Time frame: Within 6 months after enrollment
Surface DICE similarity coefficient, sDSC
Time frame: Within 6 months after enrollment
Rate of time efficiency improvement
Time frame: Within 6 months after enrollment
Volumetric revision index, VRI
Time frame: Within 6 months after enrollment
Recall, Rec
Time frame: Within 6 months after enrollment
Precision, Pre
Time frame: Within 6 months after enrollment
Relative volume difference, RVD
Time frame: Within 6 months after enrollment
Investigators satisfaction score for AI contouring
Time frame: Within 6 months after enrollment
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Feb 2026.
- Derived from this study: PubMed 41917034Niu 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.
- Individual participant dataSharing planned: Yes. The protocol of this study are available from the corresponding author upon reasonable request after the manuscript publication.
Related trials for Lung Cancer
RecruitingPhase 1/2
Study of INBRX-106 and INBRX-106 in Combination With Pembrolizumab (Keytruda®) in Subjects With Locally Advanced or Metastatic Solid Tumors (Hexavalent OX40 Agonist)
Solid Tumor, Non-Small Cell Lung Cancer
RecruitingObservational study
A Prospective ,Multicenter ,Non-interventional ,Real-world Study Observing Changes in Molecular Markers Osimertinib Targeted Therapy in EGFRm NSCLC
Non Small Cell Lung Cancer
Active, not recruiting
Streamlined Geriatric and Oncological Evaluation Based On IC Technology
Breast Cancer, Comorbidities and Coexisting Conditions
RecruitingPhase 3
A Study Evaluating Sotorasib Platinum Doublet Combination Versus Pembrolizumab Platinum Doublet Combination as a Front-Line Therapy in Participants With Stage IV or Advanced Stage IIIB/C Nonsquamous Non-Small Cell Lung Cancers (CodeBreaK 202)
Non-Small Cell Lung Cancer (NSCLC)
Active, not recruitingPhase 2
Datopotamab Deruxtecan (Dato-DXd, DS-1062a) in Advanced and/or Unresectable Non-Small Cell Lung Cancer
Metastatic Lung Cancer, Advanced Non Small Cell Lung Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Zhiyong Yuan, Ph.D. principal investigator
Tianjin Medical University Cancer Institute and Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Breast Cancer, run by Tianjin Medical University Cancer Institute and Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 12 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
volumetric DICE similarity coefficient, vDSC (time frame: Within 6 months after enrollment). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05787522, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Feb 2026.