Clinical Application Value of Deep Learning-Based "Opportunistic Screening" for Malignant Tumors on Routine Non-Contrast Chest-Abdomen-Pelvis CT

June 5, 2026 updated by: Lian Yang
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 Overview

Status

Not yet recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

100000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China, 430000
        • Union Hospital，Tongji Medical College，Huazhong University of Science and Technology
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Patients with a confirmed diagnosis of the target malignancy who received treatment.

Description

Inclusion Criteria:

  1. Patients with a confirmed diagnosis of the target malignancy who received treatment at our institution;
  2. Diagnostic-quality CT images without substantial metal or motion artifacts and with complete anatomical coverage of the target organ (breast, liver, kidney, or bladder);
  3. Availability of complete pre-treatment non-contrast CT imaging data.

Exclusion Criteria:

  1. Non-diagnostic image quality;
  2. Absence of a definitive reference-standard diagnosis;
  3. Incomplete clinical or imaging data.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Positive Group / Malignant Cohort
Negative Control Group I / Benign Cohort
Negative Control Group II / Healthy Cohort

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
diagnostic sensitivity
Time Frame: 1.5 years
1.5 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Lian Yang

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 1, 2026

Primary Completion (Estimated)

July 1, 2028

Study Completion (Estimated)

July 1, 2028

Study Registration Dates

First Submitted

June 5, 2026

First Submitted That Met QC Criteria

June 5, 2026

First Posted (Actual)

June 10, 2026

Study Record Updates

Last Update Posted (Actual)

June 10, 2026

Last Update Submitted That Met QC Criteria

June 5, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2026-0527

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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