Lung CancerNCT07829640
AI-Assisted Chest CT Interpretation Across the Lung Cancer Care Continuum
A study testing Chest CT Artificial Intelligence Decision-Support System in people with Lung Cancer, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
Trial summary
This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer
- What is being tested?
- Chest CT Artificial Intelligence Decision-Support System
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 15 participants
- Where
- 1 siteChina
- Study dates
- Sep 2026to 31 Aug 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Physicians with education or training relevant to medical imaging
- Experience in interpreting chest CT examinations
- Ability to complete the required study training and both reading sessions
- Willingness to provide written informed consent and comply with study procedures
Who cannot join
- Failure to complete the required study training
- Inability or unwillingness to complete both reading sessions as required
- Any protocol deviation likely to compromise the validity of the reader-study data
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430022
Lian Yang, PhD, +86 18986273791, yanglian@hust.edu.cn
About this study
Official title: Development of a Chest CT-Based Artificial Intelligence Model Across the Lung Cancer Care Continuum and a Prospective Randomized Crossover Reader Study of Its Clinical Decision-Support Performance
This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop, validate, and lock artificial intelligence models for lung cancer-related imaging tasks.
In the prospective stage, approximately 12 to 15 physicians with experience in chest CT interpretation will complete two reading sessions in randomized order: unaided interpretation and AI-assisted interpretation. The sessions will be separated by a washout period of at least 4 weeks. The primary objective is to compare diagnostic performance between AI-assisted and unaided interpretation. Secondary objectives include reading time, diagnostic confidence, inter-reader agreement, and errors related to incorrect AI suggestions.
All readings will be performed in an offline research environment. AI outputs will not be used for patient care, and the study will not add imaging examinations, treatment, or follow-up for patients.
- Conditions
- Lung Cancer
- Being tested
- Chest CT Artificial Intelligence Decision-Support System
- Keywords
- Artificial Intelligence, Computed Tomography, Clinical Decision Support, Diagnostic Performance, Reader Study
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Chest CT Artificial Intelligence Decision-Support SystemDevice
A locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care.
| Study group | Role | What they receive |
|---|---|---|
| Sequence AB: Unaided Then AI-Assisted Interpretation | Experimental | Participants first complete an unaided chest CT interpretation session. After a washout period of at least 4 weeks, they complete an AI-assisted interpretation session. Receives: Device: Chest CT Artificial Intelligence Decision-Support System |
| Sequence BA: AI-Assisted Then Unaided Interpretation | Experimental | Participants first complete an AI-assisted chest CT interpretation session. After a washout period of at least 4 weeks, they complete an unaided interpretation session. Receives: Device: Chest CT Artificial Intelligence Decision-Support System |
What is being measured
The primary outcome is the main question the study is designed to answer.
Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation
Diagnostic accuracy will be calculated as the proportion of case-level interpretations that agree with the prespecified reference standard based on pathology or clinical follow-up. The paired difference in diagnostic accuracy between AI-assisted and unaided interpretation will be estimated across participating physicians and cases using a multi-reader multi-case analysis and reported with a 95% confidence interval. Higher accuracy indicates better diagnostic performance.
Measured: At completion of the second reading session, after a washout period of at least 4 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Sep 2026
- Study startSep 2026 (estimated)
- Primary completion31 Aug 2028 (estimated)
- Study completion31 Aug 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Enrolment
- 15 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07829640
- Sponsor's protocol ID
- Radio-20260816
- Status last verified
- Sep 2026
- First submitted
- 14 Sep 2026
- Last updated
- 21 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Chest CT-Based Artificial Intelligence Model Across the Lung Cancer Care Continuum and a Prospective Randomized Crossover Reader Study of Its Clinical Decision-Support Performance
- ClinicalTrials.gov ID
- NCT07829640
- Acronym / sponsor ID
- Radio-20260816
- Conditions
- Lung Cancer
- Keywords
- Artificial Intelligence, Computed Tomography, Clinical Decision Support, Diagnostic Performance, Reader Study
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 15 participants (estimated)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- Sep 2026 (estimated)
- Primary completion
- 31 Aug 2028 (estimated)
- Study completion
- 31 Aug 2028 (estimated)
- First posted
- 21 Sep 2026 (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.
This investigator-initiated, single-center study consists of a retrospective artificial intelligence model-development stage and a prospective physician reader-study stage. Deidentified chest CT examinations acquired during routine clinical care between January 1, 2021, and December 31, 2024, will be used to develop, validate, and lock artificial intelligence models for lung cancer-related imaging tasks.
In the prospective stage, approximately 12 to 15 physicians with experience in chest CT interpretation will complete two reading sessions in randomized order: unaided interpretation and AI-assisted interpretation. The sessions will be separated by a washout period of at least 4 weeks. The primary objective is to compare diagnostic performance between AI-assisted and unaided interpretation. Secondary objectives include reading time, diagnostic confidence, inter-reader agreement, and errors related to incorrect AI suggestions.
All readings will be performed in an offline research environment. AI outputs will not be used for patient care, and the study will not add imaging examinations, treatment, or follow-up for patients.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Sequence AB: Unaided Then AI-Assisted Interpretation | Experimental | Device: Chest CT Artificial Intelligence Decision-Support SystemParticipants first complete an unaided chest CT interpretation session. After a washout period of at least 4 weeks, they complete an AI-assisted interpretation session. |
| Sequence BA: AI-Assisted Then Unaided Interpretation | Experimental | Device: Chest CT Artificial Intelligence Decision-Support SystemParticipants first complete an AI-assisted chest CT interpretation session. After a washout period of at least 4 weeks, they complete an unaided interpretation session. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Physicians with education or training relevant to medical imaging
- Experience in interpreting chest CT examinations
- Ability to complete the required study training and both reading sessions
- Willingness to provide written informed consent and comply with study procedures
Key exclusion criteria (3 listed)
- Failure to complete the required study training
- Inability or unwillingness to complete both reading sessions as required
- Any protocol deviation likely to compromise the validity of the reader-study data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Chest CT Artificial Intelligence Decision-Support SystemA locked research-use-only artificial intelligence system analyzes deidentified chest CT images and provides decision-support outputs to participating physicians. The system is evaluated only in an offline reader-study environment and is not used for actual patient care. | Device | Sequence AB: Unaided Then AI-Assisted Interpretation, Sequence BA: AI-Assisted Then Unaided Interpretation |
Primary outcome
Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation
Time frame: At completion of the second reading session, after a washout period of at least 4 weeks
Diagnostic accuracy will be calculated as the proportion of case-level interpretations that agree with the prespecified reference standard based on pathology or clinical follow-up. The paired difference in diagnostic accuracy between AI-assisted and unaided interpretation will be estimated across participating physicians and cases using a multi-reader multi-case analysis and reported with a 95% confidence interval. Higher accuracy indicates better diagnostic performance.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Sep 2026.
- Individual participant dataSharing planned: No
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Chest CT Artificial Intelligence Decision-Support System in people with Lung Cancer, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology 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 3 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: opening soon, as of the sponsor's last update on 21 Sep 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Lian Yang, PhD (+86 18986273791, yanglian@hust.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Difference in Diagnostic Accuracy Between AI-Assisted and Unaided Chest CT Interpretation (time frame: At completion of the second reading session, after a washout period of at least 4 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 21 Sep 2026.