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

NCT07639567 | Not Yet Recruiting

• 1 other identifier: 2026-0527
• Study Type: observational
• Target: 100,000
• Locations: 1 country
• Sites: 1

Brief 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. 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

Outcome Measures

Primary Outcomes (1)

• diagnostic sensitivity

  1.5 years

Study Arms (3)

Positive Group / Malignant Cohort

Negative Control Group I / Benign Cohort

Negative Control Group II / Healthy Cohort

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Lian Yang: lead

Study Sites (1)

Union Hospital，Tongji Medical College，Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

Location

MeSH Terms

Conditions

Neoplasms

Central Study Contacts

Lian Yang

CONTACT

18986273791; yanglian@hust.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Director

Study Record Dates

• First Submitted: June 5, 2026
• First Posted: June 10, 2026
• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): July 1, 2028
• Study Completion (Estimated): July 1, 2028
• Last Updated: June 10, 2026

Record last verified: 2026-06

Locations

CN (1)