TumorNCT07639567
Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
An observational study of Tumor, run by Lian Yang at 1 site in China.
Trial summary
This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Tumor
- How many people
- 100,000 participants
- Where
- 1 siteChina
- Study dates
- Jul 2026to Jul 2028
- Phase
- Observational
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients with a confirmed diagnosis of the target malignancy who received treatment.
Who may be able to join
- Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;
- Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);
- Availability of complete pre-treatment non-contrast CT imaging data.
Who cannot join
- Non-diagnostic image quality;
- Absence of a definitive reference-standard diagnosis;
- Incomplete clinical or imaging 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, 430000
Lian Yang, 18986273791, yanglian@hust.edu.cn
About this study
This study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.
- Conditions
- Tumor
- Medical subject headings
- 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 |
|---|---|---|
| Positive Group / Malignant Cohort | Not described | |
| Negative Control Group I / Benign Cohort | Not described | |
| Negative Control Group II / Healthy Cohort | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy
Proportion of correct classifications
Measured: 1.5 years
Sensitivity
Proportion of true positive cases
Measured: 1.5years
Specificity
Proportion of true negative cases
Measured: 1.5years
Secondary outcomes (3)
Delta Sensitivity (AI-assisted vs. unassisted)
Absolute difference in sensitivity between AI-assisted and unassisted human readings
Measured: 1.5years
Delta Specificity (AI-assisted vs. unassisted)
Absolute difference in specificity between AI-assisted and unassisted human readings
Measured: 1.5years
Delta Accuracy (AI-assisted vs. unassisted)
Absolute difference in accuracy between AI-assisted and unassisted human readings
Measured: 1.5years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jun 2026
- Study startJul 2026 (estimated)
- Primary completionJul 2028 (estimated)
- Study completionJul 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for TumorStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Lian Yang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Lian Yang, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07639567
- Sponsor's protocol ID
- 2026-0527
- Status last verified
- Jun 2026
- First submitted
- 5 Jun 2026
- Last updated
- 31 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT
- ClinicalTrials.gov ID
- NCT07639567
- Acronym / sponsor ID
- 2026-0527
- Conditions
- Tumor
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Lian YangUniversity, hospital or other
- Study start
- Jul 2026 (estimated)
- Primary completion
- Jul 2028 (estimated)
- Study completion
- Jul 2028 (estimated)
- First posted
- 10 Jun 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 study aims to develop and validate a deep learning-based opportunistic multi-cancer screening system using routine non-contrast chest-abdomen-pelvis CT examinations, including CHANCE-Breast, CHANCE-Liver, CHANCE-Kidney, and CHANCE-Bladder, for the early detection of breast, liver, kidney, and bladder cancers. In addition, the study will assess a human-AI collaborative framework to determine its potential for improving cancer detection and reducing missed diagnoses in clinical practice.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Positive Group / Malignant Cohort | – | – |
| Negative Control Group I / Benign Cohort | – | – |
| Negative Control Group II / Healthy Cohort | – | – |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with a confirmed diagnosis of the target malignancy who received treatment.
Key inclusion criteria (3 listed)
- Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;
- Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);
- Availability of complete pre-treatment non-contrast CT imaging data.
Key exclusion criteria (3 listed)
- Non-diagnostic image quality;
- Absence of a definitive reference-standard diagnosis;
- Incomplete clinical or imaging data.
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
Accuracy
Time frame: 1.5 years
Proportion of correct classifications
Sensitivity
Time frame: 1.5years
Proportion of true positive cases
Specificity
Time frame: 1.5years
Proportion of true negative cases
3 secondary outcomes
Delta Sensitivity (AI-assisted vs. unassisted)
Time frame: 1.5years
Delta Specificity (AI-assisted vs. unassisted)
Time frame: 1.5years
Delta Accuracy (AI-assisted vs. unassisted)
Time frame: 1.5years
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 31 Jul 2026.
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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?
An observational study of Tumor, run by Lian Yang 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 3 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 31 Jul 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 (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?
Accuracy (time frame: 1.5 years). The study lists 3 primary outcomes in all. 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 31 Jul 2026; AzTrials last refreshed its copy on 31 Oct 2026.