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

Starting soon

Conditions studied: Tumor

In brief

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.

Key facts

Study ID

NCT07639567

Run by

Lian Yang

People needed

100000

Starts

2026-07-01

Expected to finish

2028-07-01

Last updated by the study team

2026-07-31

Who can join

Age: any. Sex: any. Healthy volunteers: accepted.

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.

Where it is running

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

Full record on ClinicalTrials.gov

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